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We are in the process of setting enabling SSL on a soap web service exposed via InterSystems, but are running into trouble. We have installed our certificates on our webserver (Apache 2.4) and enabled SSL over the default port 57772. However, we now get an error when sending a soap message to the web service (it used to work over http). Specifically the CSP gateway refuses to route te emssage the soap web service:<SOAP-ENV:Envelope SOAP-ENV:encodingStyle="http://schemas.xmlsoap.org/soap/encoding/" xmlns:SOAP-ENV="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:s="http://www.w3.org/2001/XMLSchema"> <SOAP-ENV:Body> <SOAP-ENV:Fault> <faultcode>SOAP-ENV:Server</faultcode> <faultstring>CSP Gateway Error (version:2016.1.2.209.0 build:1601.1554e)</faultstring> <detail> <error xmlns="http://tempuri.org"> <special>Systems Management</special> <text>Invalid Request : Cannot identify application path</text> </error> </detail> </SOAP-ENV:Fault> </SOAP-ENV:Body></SOAP-ENV:Envelope>Probably either the CSP gateway or the web server was misconfigured. Anyone an idea in which direction we might look. (BTW accessing the management port now returns the same error as does using SSL port 443).PS this issue was also submitted to WRC Tom,I presume by now you've had this answered by the WRC, but the issue is most likely that the private Apache web server that ships with Caché/Ensemble does not currently support SSL. In order to configure SSL, you would need to configure a full Apache or IIS web server, which is typically recommended for any public-facing, production-level deployment anyway.-Steve

InterSystems is pleased to announce the availability of InterSystems IRIS Data Platform 2018.1.2 maintenance release For information about the corrections in this release, refer to the release notes.This release is supported on the same platforms as InterSystems IRIS 2018.1.1. You can see details, including cloud platforms and docker containers supported, in this Supported Platforms document. The build corresponding to this release is 2018.1.2.609.0 If you have not visited our Learning Services site recently, I encourage you to try the InterSystems IRIS sandbox and Experiences.

From the first glance, the task of configuring LDAP authentication in Caché is not hard at all – the manual describes this process in just 6 paragraphs. On the other hand, if the LDAP server uses Microsoft Active Directory, there a few non-evident things that need to be configured on the LDAP server side. Those who don’t do anything like that on a regular basis may get lost in Caché settings. In this article, we will describe the step-by-step process of setting up LDAP authentication and cover the diagnostic methods that can be used if something doesn’t work as expected.Configuration of the LDAP server1. Create a user in ActiveDirectory that we will use to connect to Caché and search for information in the LDAP database. This user must be located in the domain’s root.2. Let’s create a special unit for users who will be connecting to Caché and call it IdapCacheUsers.3. Register users there.4. Let’s test the availability of the LDAP database using a tool called ldapAdmin. You can download it here.5. Configure the connection to the LDAP server:6. All right, we are connected now. Let’s take a look at how it all works:7. Since users that will be connecting to Caché are in the ldapCacheUsers unit, let’s limit our search to this unit only.Settings on the Caché side8. The LDAP server is ready, so let’s proceed to configuring the settings on the Caché side. Go to Management Portal -> System Administration -> Security -> System Security -> LDAP Options. Let’s clear the “User attribute to retrieve default namespace”, “User attribute to retrieve default routine” and “User attribute to retrieve roles” fields, since these attributes are not in the LDAP database yet.9. Enable LDAP authentication in System Administration -> Security -> System security -> Authentication/CSP Session Settings10. Enable LDAP authentication in services. The %Service_CSP service is responsible for connecting web applications, %Service_Console handles connections through the terminal.11. Configure LDAP authentication in web applications.12. For the time being and for testing the connection, let’s configure everything so that new users in Caché have full rights. To do this, assign the %All role to the user _PUBLIC. We will address this aspect in the future ……13. Let’s try opening the configured web application, it should open without problems.14. The terminal also opens15. After connecting, LDAP users will appear on the Caché users list16. The truth is, this configuration gives all new users complete access to the system. To close this security hole, we need to modify the LDAP database by adding an attribute that we will use to store the name of the role that will be assigned to users after connecting to Caché. Prior to that, we need to make a backup copy of the domain controller to ensure that we don’t break the entire network if something goes wrong with the configuration process.17. To modify the ActiveDirectory schema, let’s install the Active Directory snap-in on the server where ActiveDirectory is installed (it is not installed by default). Read the instruction here.18. Let’s create an attribute called intersystems-Roles, OID 1.2.840.113556.1.8000.2448.2.3, a case-sensitive string, a multi-value attribute.19. Then add this attribute to the class “user”.20. Let’s now make it so that when we view the list of unit users, we can see a “Role in InterSystems Cache” column. To do that, click Start -> Run and type “adsiedit.msc”. We are connecting to “Configuration” naming context.21. Let’s go to the CN=409, CN=DisplaySpecifiers, CN=Configuration container and choose a container type that will show additional user attributes when we view it. Let’s choose unit-level display (OU) provided by the organisationalUnit-Display container. We need to find the extraColumns attribute in its properties and change its value to ”intersystems-Roles, Role in IntersystemsCache,1,200,0”. The rule of composing the attribute is as follows: attribute name, name of the destination column, display by default or not, column width in pixels, reserved value. One more comment: CN=409 denotes a language code (CN=409 for the English version, CN=419 for the Russian version of the console).22. We can now fill out the name of the role that will be assigned to all users connecting to Caché. If your Active Directory is running on Windows Server 2003, you won’t have any built-in tools for editing this field. You can use a tool called ldapAdmin (see item 4) for editing the value of this attribute. If you have a newer version of Windows, this attribute can be edited in the “Additional functions” mode – the user will see an additional tab for editing attributes.23. After that, let’s specify the name of this attribute in the LDAP options on the Caché management portal. 24. Let’s create an ldapRole with the necessary privileges25. Remove the %ALL role from the user _PUBLIC26. Everything is set up, let’s try connecting to the system27. If it doesn’t work right away, enable and set up an audit28. Audit settings29. Look at the error log in Audit Database.ConclusionIn reality, it often happens that the configuration of different roles for different users is not required for working in an application. If you only need to assign a particular set of permissions to users logging in to a web application, you can skip steps 16 through 23. All you will need to do is to add these roles and remove all types of authentication except for LDAP on the “Application roles” tab in the web application settings. In this case, only users registered on the LDAP sever can log in. When such a user logs in, Caché automatically assigns the roles required for working in this application. I wanted to add that you certainly can create an attribute to list a user's roles as described here, and some sites do, but it's not the only way to configure LDAP authentication. Many administrators find the group-based behavior enabled by the "Use LDAP Groups for Roles/Routine/Namespace" option easier to configure, so you should consider that option if you're setting up LDAP authentication. If you do use that option, many of the steps here will be different, including at least steps 17-23 where the attribute is created and configured. Yes, I agree. Thanks for the addition Thank you for sharing. Good job.

Hi all. We are going to find duplicates in a dataset using Apache Spark Machine Learning algorithms. Note: I have done the following on Ubuntu 18.04, Python 3.6.5, Zeppelin 0.8.0, Spark 2.1.1 Introduction In previous articles we have done the following: The way to launch Jupyter Notebook + Apache Spark + InterSystems IRIS Load a ML model into InterSystems IRIS K-Means clustering of the Iris Dataset The way to launch Apache Spark + Apache Zeppelin + InterSystems IRIS In this series of articles, we explore Machine Learning and record linkage. Imagine that we merged databases of neighboring shops. Most probably there will be records that are very similar to each over. Some records will be of the same person and we call them duplicates. Our purpose is to find duplicates. Why is this necessary? First of all, to combine data from many different operational source systems into one logical data model, which can then be subsequently fed into a business intelligence system for reporting and analytics. Secondly, to reduce data storage costs. There are some additional use cases. Approach What data do we have? Each row contains different anonymized information about one person. There are family names, given names, middle names, date of births, several documents, etc. The first step is to look at the number of records because we are going to make pairs. The number of pairs equals n*(n-1)/2. So, if you have less than 5000 records, than the number of pairs would be 12.497.500. It is not that many, so we can pair each record. But if you have 50.000, 100.000 or more, the number of pairs more than a billion. This number of pairs is hard to store and work with. So, if you have a lot of records, it would be a good idea to reduce this number. We will do it by selecting potential duplicates. A potential duplicate is a pair, that might be a duplicate. We will detect them based on several simple conditions. A specific condition might be like: (record1.family_name == record2.familyName) & (record1.givenName == record2.givenName) & (record1.dateOfBirth == record2.dateOfBirth) but keep in mind that you can miss duplicates because of strict logical conditions. I think the optimal solution is to choose important conditions and use no more than two of them with & operator. But you should convert each feature into one record shape beforehand. For example, there are several ways to store dates: 1985-10-10, 10/10/1985, etc convert to 10-10-1985(month-day-year). The next step is to label the part of the dataset. We will randomly choose, for example, 5000-10000 pairs (or more, if you are sure that you can label all of them). We will save them to IRIS and label these pairs in Jupyter (Unfortunately, I didn't find an easy and convenient way to do it. Also, you can label them in PySpark console or wherever you want). After that, we will make a feature vector for each pair. During the labeling process probably you noticed which features are important and what they equal. So, test different approaches to creating feature vectors. Test different machine learning models. I chose a random forest model because of tests (accuracy/precision/recall/etc). Also, you can try decision trees, Naive Bayes, other classification model and choose the one that will be the best. Test the result. If you are not satisfied with the result, try to change feature vectors or change a ML model. Finally, fit all pairs into the model and look at the result. Implementation Load a dataset: %pysparkdataFrame=spark.read.format("com.intersystems.spark").option("url", "IRIS://localhost:51773/******").option("user", "*******").option("password", "*********************").option("dbtable", "**************").load() Clean the dataset. For example, null (check every row) or useless columns: %pysparkcolumns_to_drop = ['allIdentityDocuments', 'birthCertificate_docSource', 'birthCertificate_expirationDate', 'identityDocument_expirationDate', 'fullName']droppedDF = dataFrame.drop(*columns_to_drop) Prepare the dataset for making pairs: %pysparkfrom pyspark.sql.functions import col# rename columns namesreplacements1 = {c : c + '1' for c in droppedDF.columns}df1 = droppedDF.select([col(c).alias(replacements1.get(c, c)) for c in droppedDF.columns])replacements2 = {c : c + '2' for c in droppedDF.columns}df2 = droppedDF.select([col(c).alias(replacements2.get(c, c)) for c in droppedDF.columns]) To make pairs we will use join function with several conditions. %pysparktestTable = (df1.join(df2, (df1.ID1 < df2.ID2) & ( (df1.familyName1 == df2.familyName2) & (df1.givenName1 == df2.givenName2) | (df1.familyName1 == df2.familyName2) & (df1.middleName1 == df2.middleName2) | (df1.familyName1 == df2.familyName2) & (df1.dob1 == df2.dob2) | (df1.familyName1 == df2.familyName2) & (df1.snils1 == df2.snils2) | (df1.familyName1 == df2.familyName2) & (df1.addr_addressLine1 == df2.addr_addressLine2) | (df1.familyName1 == df2.familyName2) & (df1.addr_okato1 == df2.addr_okato2) | (df1.givenName1 == df2.givenName2) & (df1.middleName1 == df2.middleName2) | (df1.givenName1 == df2.givenName2) & (df1.dob1 == df2.dob2) | (df1.givenName1 == df2.givenName2) & (df1.snils1 == df2.snils2) | (df1.givenName1 == df2.givenName2) & (df1.addr_addressLine1 == df2.addr_addressLine2) | (df1.givenName1 == df2.givenName2) & (df1.addr_okato1 == df2.addr_okato2) | (df1.middleName1 == df2.middleName2) & (df1.dob1 == df2.dob2) | (df1.middleName1 == df2.middleName2) & (df1.snils1 == df2.snils2) | (df1.middleName1 == df2.middleName2) & (df1.addr_addressLine1 == df2.addr_addressLine2) | (df1.middleName1 == df2.middleName2) & (df1.addr_okato1 == df2.addr_okato2) | (df1.dob1 == df2.dob2) & (df1.snils1 == df2.snils2) | (df1.dob1 == df2.dob2) & (df1.addr_addressLine1 == df2.addr_addressLine2) | (df1.dob1 == df2.dob2) & (df1.addr_okato1 == df2.addr_okato2) | (df1.snils1 == df2.snils2) & (df1.addr_addressLine1 == df2.addr_addressLine2) | (df1.snils1 == df2.snils2) & (df1.addr_okato1 == df2.addr_okato2) | (df1.addr_addressLine1 == df2.addr_addressLine2) & (df1.addr_okato1 == df2.addr_okato2) ))) Check the size of returned dataframe: %pysparkdroppedColumns = ['prevIdentityDocuments1', 'birthCertificate_docDate1', 'birthCertificate_docNum1', 'birthCertificate_docSer1', 'birthCertificate_docType1', 'identityDocument_docDate1', 'identityDocument_docNum1', 'identityDocument_docSer1', 'identityDocument_docSource1', 'identityDocument_docType1', 'prevIdentityDocuments2', 'birthCertificate_docDate2', 'birthCertificate_docNum2', 'birthCertificate_docSer2', 'birthCertificate_docType2', 'identityDocument_docDate2', 'identityDocument_docNum2', 'identityDocument_docSer2', 'identityDocument_docSource2', 'identityDocument_docType2'] print(testTable.count())testTable.drop(*droppedColumns).show() # I dropped several columns just for show() function Randomly take a part of the dataframe: %pysparkrandomDF = testTable.sample(False, 0.33, 0)randomDF.write.format("com.intersystems.spark").\option("url", "IRIS://localhost:51773/DEDUPL").\option("user", "*****").option("password", "***********").\option("dbtable", "deduplication.unlabeledData").save() Label pairs in Jupyter Run the following (it will widen the cells). from IPython.core.display import display, HTMLdisplay(HTML("<style>.container { width:100% !important; border-left-width: 1px !important; resize: vertical}</style>")) Load dataframe: unlabeledDF = spark.read.format("com.intersystems.spark").option("url", "IRIS://localhost:51773/DEDUPL").option("user", "********").option("password", "**************").option("dbtable", "deduplication.unlabeledData").load() Return all the elements of the dataset as a list: rows = labelledDF.collect() The convenient way to display pairs: from IPython.display import clear_outputfrom prettytable import PrettyTablefrom collections import OrderedDict def printTable(row): row = OrderedDict((k, row.asDict()[k]) for k in newColumns) table = PrettyTable() column_names = ['Person1', 'Person2'] column1 = [] column2 = [] i = 0 for key, value in row.items(): if key != 'ID1' and key != 'ID2' and key != "prevIdentityDocuments1" and key != 'prevIdentityDocuments2' and key != "features": if (i < 20): column1.append(value) else: column2.append(value) i += 1 table.add_column(column_names[0], column1) table.add_column(column_names[1], column2) print(table) List where we will store rows: listDF = [] The labeling process: from pyspark.sql import Rowfrom IPython.display import clear_outputimport time# 3000 - 4020for number in range(3000 + len(listDF), len(rows)): row = rows[number] if (len(listDF) % 10) == 0: print(3000 + len(listDF)) printTable(row) result = 0 label = 123 while True: result = input('duplicate? y|n|stop') if (result == 'stop'): break elif result == 'y': label = 1.0 break elif result == 'n': label = 0.0 break else: print('only y|n|stop') continue if result == 'stop': break tmp = row.asDict() tmp['label'] = label newRow = Row(**tmp) listDF.append(newRow) time.sleep(0.2) clear_output() Create a dataframe again: newColumns.append('label')labelledDF = spark.createDataFrame(listDF).select(*newColumns) Save it to IRIS: labeledDF.write.format("com.intersystems.spark").\option("url", "IRIS://localhost:51773/DEDUPL").\option("user", "***********").option("password", "**********").\option("dbtable", "deduplication.labeledData").save() Feature vector and ML model Load a dataframe into Zeppelin: %pysparklabeledDF = spark.read.format("com.intersystems.spark").option("url", "IRIS://localhost:51773/DEDUPL").option("user", "********").option("password", "***********").option("dbtable", "deduplication.labeledData").load() Feature vector generation: %pysparkfrom pyspark.sql.functions import udf, structimport stringdistfrom pyspark.sql.types import StructType, StructField, StringType, IntegerType, DateType, ArrayType, FloatType, DoubleType, LongType, NullTypefrom pyspark.ml.linalg import Vectors, VectorUDTimport roman translateMap = {'A' : 'А', 'B' : 'В', 'C' : 'С', 'E' : 'Е', 'H' : 'Н', 'K' : 'К', 'M' : 'М', 'O' : 'О', 'P' : 'Р', 'T' : 'Т', 'X' : 'Х', 'Y' : 'У'} column_names = testTable.drop('ID1').drop('ID2').columnscolumnsSize = len(column_names)//2 def isRoman(numeral): numeral = numeral.upper() validRomanNumerals = ["M", "D", "C", "L", "X", "V", "I", "(", ")"] for letters in numeral: if letters not in validRomanNumerals: return False return True def differenceVector(params): differVector = [] for i in range(0, 3): if params[i] == None or params[columnsSize + i] == None: differVector.append(0.0) elif params[i] == 'НЕТ' or params[columnsSize + i] == 'НЕТ': differVector.append(0.0) elif params[i][:params[columnsSize + i].find('-')] == params[columnsSize + i][:params[columnsSize + i].find('-')] or params[i][:params[i].find('-')] == params[columnsSize + i][:params[i].find('-')]: differVector.append(0.0) else: differVector.append(stringdist.levenshtein(params[i], params[columnsSize+i])) for i in range(3, columnsSize): # snils if i == 5 or i == columnsSize + 5: if params[i] == None or params[columnsSize + i] == None or params[i].find('123-456-789') != -1 or params[i].find('111-111-111') != -1 \ or params[columnsSize + i].find('123-456-789') != -1 or params[columnsSize + i].find('111-111-111') != -1: differVector.append(0.0) else: differVector.append(float(params[i] != params[columnsSize + i])) # birthCertificate_docNum elif i == 10 or i == columnsSize + 10: if params[i] == None or params[columnsSize + i] == None or params[i].find('000000') != -1 or params[i].find('000000') != -1 \ or params[columnsSize + i].find('000000') != -1 or params[columnsSize + i].find('000000') != -1: differVector.append(0.0) else: differVector.append(float(params[i] != params[columnsSize + i])) # birthCertificate_docSer elif i == 11 or i == columnsSize + 11: if params[i] == None or params[columnsSize + i] == None: differVector.append(0.0) # check if roman or not, then convert if roman else: docSer1 = params[i] docSer2 = params[columnsSize + i] if isRoman(params[i][:params[i].index('-')]): docSer1 = str(roman.fromRoman(params[i][:params[i].index('-')])) secPart1 = '-' for elem in params[i][params[i].index('-') + 1:]: if 65 <= ord(elem) <= 90: secPart1 += translateMap[elem] else: secPart1 = params[i][params[i].index('-'):] docSer1 += secPart1 if isRoman(params[columnsSize + i][:params[columnsSize + i].index('-')]): docSer2 = str(roman.fromRoman(params[columnsSize + i][:params[columnsSize + i].index('-')])) secPart2 = '-' for elem in params[columnsSize + i][params[columnsSize + i].index('-') + 1:]: if 65 <= ord(elem) <= 90: secPart2 += translateMap[elem] else: secPart2 = params[columnsSize + i][params[columnsSize + i].index('-'):] break docSer2 += secPart2 differVector.append(float(docSer1 != docSer2)) elif params[i] == 0 or params[columnsSize + i] == 0: differVector.append(0.0) elif params[i] == None or params[columnsSize + i] == None: differVector.append(0.0) else: differVector.append(float(params[i] != params[columnsSize + i])) return differVector featuresGenerator = udf(lambda input: Vectors.dense(differenceVector(input)), VectorUDT()) %pysparknewTestTable = testTable.withColumn('features', featuresGenerator(struct(*column_names))) # all pairsdf = df.withColumn('features', featuresGenerator(struct(*column_names))) # labeled pairs Split labeled dataframe into training and test dataframes: %pysparkfrom pyspark.ml import Pipelinefrom pyspark.ml.classification import RandomForestClassifierfrom pyspark.ml.feature import IndexToString, StringIndexer, VectorIndexerfrom pyspark.ml.evaluation import MulticlassClassificationEvaluator # split labelled data into two sets(trainingData, testData) = df.randomSplit([0.7, 0.3]) Train a RF model: %pysparkfrom pyspark.ml.classification import RandomForestClassifier rf = RandomForestClassifier(labelCol='label', featuresCol='features') pipeline = Pipeline(stages=[rf]) model = pipeline.fit(trainingData) # Make predictions.predictions = model.transform(testData)# predictions.select("predictedLabel", "label", "features").show(5) Test the RF model: %pysparkTP = int(predictions.select("label", "prediction").where((col("label") == 1) & (col('prediction') == 1)).count())TN = int(predictions.select("label", "prediction").where((col("label") == 0) & (col('prediction') == 0)).count())FP = int(predictions.select("label", "prediction").where((col("label") == 0) & (col('prediction') == 1)).count())FN = int(predictions.select("label", "prediction").where((col("label") == 1) & (col('prediction') == 0)).count())total = int(predictions.select("label").count()) print("accuracy = %f" % ((TP + TN) / total))print("precision = %f" % (TP/ (TP + FP))print("recall = %f" % (TP / (TP + FN)) How it looks: Use the RF model on all the pairs: %pysparkallData = model.transform(newTestTable) Check how many duplicates are found: %pysparkallData.where(col('prediction') == 1).count() Or look at the dataframe: Conclusion This approach is not ideal. You can make it better by experimenting with feature vectors, a model or increasing the size of labeled dataset. Also, you can do the same to find duplicates, for example, in shops database, historical research, etc... Links Apache Zeppelin Jupyter Notebook Apache Spark Record Linkage ML models The way to launch Jupyter Notebook + Apache Spark + InterSystems IRIS Load a ML model into InterSystems IRIS K-Means clustering of the Iris Dataset The way to launch Apache Spark + Apache Zeppelin + InterSystems IRIS GitHub

Google Cloud Platform (GCP) provides a feature rich environment for Infrastructure-as-a-Service (IaaS) as a cloud offering fully capable of supporting all of InterSystems products including the latest InterSystems IRIS Data Platform. Care must be taken, as with any platform or deployment model, to ensure all aspects of an environment are considered such as performance, availability, operations, and management procedures. Specifics of each of those areas will be covered in this article. The following overview and details are provided by Google and can be found here. Overview GCP Resources GCP consists of a set of physical assets, such as computers and hard disk drives, and virtual resources, such as virtual machines (VMs), that are contained in Google's data centers around the globe. Each data center location is in a global region. Each region is a collection of zones, which are isolated from each other within the region. Each zone is identified by a name that combines a letter identifier with the name of the region. This distribution of resources provides several benefits, including redundancy in case of failure and reduced latency by locating resources closer to clients. This distribution also introduces some rules about how resources can be used together. Accessing GCP Resources In cloud computing physical hardware and software become services. These services provide access to the underlying resources. When you develop your InterSytems IRIS-based application on GCP, you mix and match these services into combinations that provide the infrastructure you need, and then add your code to enable the scenarios you want to build. Details of the available services can be found here. Projects Any GCP resources that you allocate and use must belong to a project. A project is made up of the settings, permissions, and other metadata that describe your applications. Resources within a single project can work together easily, for example by communicating through an internal network, subject to the regions-and-zones rules. The resources that each project contains remain separate across project boundaries; you can only interconnect them through an external network connection. Interacting with Services GCP gives you three basic ways to interact with the services and resources. Console The Google Cloud Platform Console provides a web-based, graphical user interface that you can use to manage your GCP projects and resources. When you use the GCP Console, you create a new project, or choose an existing project, and use the resources that you create in the context of that project. You can create multiple projects, so you can use projects to separate your work in whatever way makes sense for you. For example, you might start a new project if you want to make sure only certain team members can access the resources in that project, while all team members can continue to access resources in another project. Command-line Interface If you prefer to work in a terminal window, the Google Cloud SDK provides the gcloud command-line tool, which gives you access to the commands you need. The gcloud tool can be used to manage both your development workflow and your GCP resources. gcloud reference details can be found here. GCP also provides Cloud Shell, a browser-based, interactive shell environment for GCP. You can access Cloud Shell from the GCP console. Cloud Shell provides: A temporary Compute Engine virtual machine instance. Command-line access to the instance from a web browser. A built-in code editor. 5 GB of persistent disk storage. Pre-installed Google Cloud SDK and other tools. Language support for Java, Go, Python, Node.js, PHP, Ruby and .NET. Web preview functionality. Built-in authorization for access to GCP Console projects and resources. Client Libraries The Cloud SDK includes client libraries that enable you to easily create and manage resources. GCP client libraries expose APIs for two main purposes: App APIs provide access to services. App APIs are optimized for supported languages, such as Node.js and Python. The libraries are designed around service metaphors, so you can work with the services more naturally and write less boilerplate code. The libraries also provide helpers for authentication and authorization. Details can be found here. Admin APIs offer functionality for resource management. For example, you can use admin APIs if you want to build your own automated tools. You also can use the Google API client libraries to access APIs for products such as Google Maps, Google Drive, and YouTube. Details of GCP client libraries can be found here. InterSystems IRIS Sample Architectures As part of this article, sample InterSystems IRIS deployments for GCP are provided as a starting point for your application specific deployment. These can be used as a guideline for numerous deployment possibilities. This reference architecture demonstrates highly robust deployment options starting with the smallest deployments to massively scalable workloads for both compute and data requirements. High availability and disaster recovery options are covered in this document along with other recommended system operations. It is expected these will be modified by the individual to support their organization’s standard practices and security policies. InterSystems is available for further discussions or questions of GCP-based InterSystems IRIS deployments for your specific application. Sample Reference Architectures The following sample architectures will provide several different configurations with increasing capacity and capabilities. Consider these examples of small development / production / large production / production with sharded cluster that show the progression from starting with a small modest configuration for development efforts and then growing to massively scalable solutions with proper high availability across zones and multi-region disaster recovery. In addition, an example architecture of using the new sharding capabilities of InterSystems IRIS Data Platform for hybrid workloads with massively parallel SQL query processing. Small Development Configuration In this example, a minimal configuration is used to illustrates a small development environment capable of supporting up to 10 developers and 100GB of data. More developers and data can easily be supported by simply changing the virtual machine instance type and increasing storage of the persistent disks as appropriate. This is adequate to support development efforts and become familiar with InterSystems IRIS functionality along with Docker container building and orchestration if desired. High availability with database mirroring is typically not used with a small configuration, however it can be added at any time if high availability is needed. Small Configuration Sample Diagram The below sample diagram in Figure 2.1.1-a illustrates the table of resources in Figure 2.1.1-b. The gateways included are just examples, and can be adjusted accordingly to suit your organization’s standard network practices. The following resources within the GCP VPC Project are provisioned as a minimum small configuration. GCP resources can be added or removed as required. Small Configuration GCP Resources Sample of Small Configuration GCP resources is provided below in the following table. Proper network security and firewall rules need to be considered to prevent unwanted access into the VPC. Google provides network security best practices for getting started which can be found here. Note: VM instances require a public IP address to reach GCP services. While this practice might raise some concerns, Google recommends limiting the incoming traffic to these VM instances by using firewall rules. If your security policy requires truly internal VM instances, you will need to set up a NAT proxy manually on your network and a corresponding route so that the internal instances can reach the Internet. It is important to note that you cannot connect to a fully internal VM instance directly by using SSH. To connect to such internal machines, you must set up a bastion instance that has an external IP address and then tunnel through it. A bastion Host can be provisioned to provide the external facing point of entry into your VPC. Details of bastion hosts can he found here. Production Configuration In this example, a more sizable configuration as an example production configuration that incorporates InterSystems IRIS database mirroring capability to support high availability and disaster recovery. Included in this configuration is a synchronous mirror pair of InterSystems IRIS database servers split between two zones within region-1 for automatic failover, and a third DR asynchronous mirror member in region-2 for disaster recovery in the unlikely event an entire GCP region is offline. The InterSystems Arbiter and ICM server deployed in a separate third zone for added resiliency. The sample architecture also includes a set of optional load balanced web servers to support a web-enabled application. These web servers with the InterSystems Gateway can be scaled independently as needed. Production Configuration Sample Diagram The below sample diagram in Figure 2.2.1-a illustrates the table of resources found in Figure 2.2.1-b. The gateways included are just examples, and can be adjusted accordingly to suit your organization’s standard network practices. The following resources within the GPC VPC Project are recommended as a minimum recommendation to support a sharded cluster. GCP resources can be added or removed as required. Production Configuration GCP Resources Sample of Production Configuration GCP resources is provided below in the following tables. Large Production Configuration In this example, a massively scaled configuration is provided by expanding on the InterSystems IRIS capability to also introduce application servers using InterSystems’ Enterprise Cache Protocol (ECP) to provide massive horizontal scaling of users. An even higher level of availability is included in this example because of ECP clients preserving session details even in the event of a database instance failover. Multiple GCP zones are used with both ECP-based application servers and database mirror members deployed in multiple regions. This configuration is capable of supporting tens of millions database accesses per second and multiple terabytes of data. Large Production Configuration Sample Diagram The sample diagram in Figure 2.3.1-a illustrates the table of resources in Figure 2.3.1-b. The gateways included are just examples, and can be adjusted accordingly to suit your organization’s standard network practices. Included in this configuration is a failover mirror pair, four or more ECP clients (application servers), and one or more web servers per application server. The failover database mirror pairs are split between two different GCP zones in the same region for fault domain protection with the InterSystems Arbiter and ICM server deployed in a separate third zone for added resiliency. Disaster recovery extends to a second GCP region and zone(s) similar to the earlier example. Multiple DR regions can be used with multiple DR Async mirror member targets if desired. The following resources within the GPC VPC Project are recommended as a minimum recommendation to support a large production deployment. GCP resources can be added or removed as required. Large Production Configuration GCP Resources Sample of Large Production Configuration GCP resources is provided below in the following tables. Production Configuration with InterSystems IRIS Sharded Cluster In this example, a horizontally scaled configuration for hybrid workloads with SQL is provided by including the new sharded cluster capabilities of InterSystems IRIS to provide massive horizontal scaling of SQL queries and tables across multiple systems. Details of InterSystems IRIS sharded cluster and its capabilities are discussed later in this article. Production Configuration with InterSystems IRIS Sharded Cluster The sample diagram in Figure 2.4.1-a illustrates the table of resources in Figure 2.4.1-b. The gateways included are just examples, and can be adjusted accordingly to suit your organization’s standard network practices. Included in this configuration are four mirror pairs as the data nodes. Each of the failover database mirror pairs are split between two different GCP zones in the same region for fault domain protection with the InterSystems Arbiter and ICM server deployed in a separate third zone for added resiliency. This configuration allows for all the database access methods to be available from any data node in the cluster. The large SQL table(s) data is physically partitioned across all data nodes to allow for massive parallelization of both query processing and data volume. Combining all these capabilities provides the ability to support complex hybrid workloads such as large-scale analytical SQL querying with concurrent ingestion of new data, all within a single InterSystems IRIS Data Platform. Note that in the above diagram and the “resource type” column in the table below, the term “Compute [Engine]” is a GCP term representing a GCP (virtual) server instance as described further in section 3.1 of this document. It does not represent or imply the use of “compute nodes” in the cluster architecture described later in this article. The following resources within the GPC VPC Project are recommended as a minimum recommendation to support a sharded cluster. GCP resources can be added or removed as required. Production with Sharded Cluster Configuration GCP Resources Sample of Sharded Cluster Configuration GCP resources is provided below in the following table. Introduction of Cloud Concepts Google Cloud Platform (GCP) provides a feature rich cloud environment for Infrastructure-as-a-Service (IaaS) fully capable of supporting all of InterSystems products including support for container-based DevOps with the new InterSystems IRIS Data Platform. Care must be taken, as with any platform or deployment model, to ensure all aspects of an environment are considered such as performance, availability, system operations, high availability, disaster recovery, security controls, and other management procedures. This document will cover the three major components of all cloud deployments: Compute, Storage, and Networking. Compute Engines (Virtual Machines) Within GCP there are several options available for compute engine resources with numerous virtual CPU and memory specifications and associated storage options. One item to note within GCP, references to the number of vCPUs in a given machine type equates to one vCPU is one hyper-thread on the physical host at the hypervisor layer. For the purposes of this document n1-standard* and n1-highmem* instance types will be used and are most widely available in most GCP deployment regions. However, the use of n1-ultramem* instance types are great options for very large working datasets keeping massive amounts of data cached in memory. Default instance settings such as Instance Availability Policy or other advanced features are used except where noted. Details of the various machine types can be found here. Disk Storage The storage type most directly related to InterSystems products are the persistent disk types, however local storage may be used for high levels of performance as long as data availability restrictions are understood and accommodated. There are several other options such as Cloud Storage (buckets), however those are more specific to an individual application’s requirements rather than supporting the operation of InterSystems IRIS Data Platform. Like most other cloud providers, GCP imposes limitations on the amount of persistent storage that can be associated to an individual compute engine. These limits include the maximum size of each disk, the number of persistent disks attached to each compute engine, and the amount of IOPS per persistent disk with an overall individual compute engine instance IOPS cap. In addition, there are imposed IOPS limits per GB of disk space, so at times provisioning more disk capacity is required to achieve desired IOPS rate. These limits may change over time and to be confirmed with Google as appropriate. There are two types of persistent storage types for disk volumes: Standard Persistent and SSD Persistent disks. SSD Persistent disks are more suited for production workloads that require predictable low-latency IOPS and higher throughput. Standard Persistent disks are more an economical option for non-production development and test or archive type workloads. Details of the various disk types and limitations can be found here. VPC Networking The virtual private cloud (VPC) network is highly recommended to support the various components of InterSystems IRIS Data Platform along with providing proper network security controls, various gateways, routing, internal IP address assignments, network interface isolation, and access controls. An example VPC will be detailed in the examples provided within this document. Details of VPC networking and firewalls can be found here. Virtual Private Cloud (VPC) Overview GCP VPC’s are slightly different than other cloud providers allowing for simplicity and greater flexibility. A comparison of concepts can be found here. Within a GCP project, several VPCs per project are allowed (currently a max of 5 per project), and there are two options for creating a VPC network – auto mode and custom mode. Details of each type are provided here. In most large cloud deployments, multiple VPCs are provisioned to isolate the various gateways types from application-centric VPCs and leverage VPC peering for inbound and outbound communications. It is highly recommended to consult with your network administrator for details on allowable subnets and any organizational firewall rules of your company. VPC peering is not covered in this document. In the examples provided in this document, a single VPC with three subnets will be used to provide network isolation of the various components for predictable latency and bandwidth and security isolation of the various InterSystems IRIS components. Network Gateway and Subnet Definitions Two gateways are provided in the example in this document to support both Internet and secure VPN connectivity. Each ingress access is required to have appropriate firewall and routing rules to provide adequate security for the application. Details on how to use routes can be found here. Three subnets are used in the provided example architectures dedicated for use with InterSystems IRIS Data Platform. The use of these separate network subnets and network interfaces allows for flexibility in security controls and bandwidth protection and monitoring for each of the three above major components. Details on the various use cases can be found here. Details for creating virtual machine instances with multiple network interfaces can be found here. The subnets included in these examples: User Space Network for Inbound connected users and queries Shard Network for Inter-shard communications between the shard nodes Mirroring Network for high availability using synchronous replication and automatic failover of individual data nodes. Note: Failover synchronous database mirroring is only recommended between multiple zones which have low latency interconnects within a single GCP region. Latency between regions is typically too high for to provide a positive user experience especially for deployment with a high rate of updates. Internal Load Balancers Most IaaS cloud providers lack the ability to provide for a Virtual IP (VIP) address that is typically used in automatic database failover designs. To address this, several of the most commonly used connectivity methods, specifically ECP clients and Web Gateways, are enhanced within InterSystems IRIS to no longer rely on VIP capabilities making them mirror-aware and automatic. Connectivity methods such as xDBC, direct TCP/IP sockets, or other direct connect protocols, require the use of a VIP-like address. To support those inbound protocols, InterSystems database mirroring technology makes it possible to provide automatic failover for those connectivity methods within GCP using a health check status page called mirror_status.cxw to interact with the load balancer to achieve VIP-like functionality of the load balancer only directing traffic to the active primary mirror member, thus providing a complete and robust high availability design within GCP. Details of using a load balancer to provide VIP-like functionality is provided here. Sample VPC Topology Combining all the components together, the following illustration in Figure 4.3-a demonstrates the layout of a VPC with the following characteristics: Leverages multiple zones within a region for high availability Provides two regions for disaster recovery Utilizes multiple subnets for network segregation Includes separate gateways for both Internet and VPN connectivity Uses cloud load balancer for IP failover for mirror members Persistent Storage Overview As discussed in the introduction, the use of GCP persistent disks is recommended and specifically SSD persistent disk types. SSD persistent disks are recommended due to the higher read and write IOPS rates and low latency required for transactional and analytical database workloads. Local SSDs may be used in certain circumstances, however beware that the performance gains of local SSDs comes with certain trade-offs in availability, durability, and flexibility. Details of Local SSD data persistence can be found here to understand the events of when Local SSD data is preserved and when not. LVM Striping Like other cloud providers, GCP imposes numerous limits on storage both in IOPS, space capacity, and number of devices per virtual machine instance. Consult GCP documentation for current limits which can be found here. With these limits, LVM striping becomes necessary to maximize IOPS beyond that of a single disk device for a database instance. In the example virtual machine instances provided, the following disk layouts are recommended. Performance limits associated with SSD persistent disks can be found here. Note: There is currently a maximum of 16 persistent disks per virtual machine instance although GCP currently lists an increase to 128 is “(beta)” at the moment, so this will be a welcomed enhancement. The benefits of LVM striping allows for spreading out random IO workloads to more disk devices and inherit disk queues. Below is an example of how to use LVM striping with Linux for the database volume group. This example will use four disks in an LVM PE stripe with a physical extent (PE) size of 4MB. Alternatively, larger PE sizes can be used if needed. Step 1: Create Standard or SSD Persistent Disks as needed Step 2: IO scheduler is NOOP for each of the disk devices using “lsblk -do NAME,SCHED” Step 3: Identify disk devices using “lsblk -do KNAME,TYPE,SIZE,MODEL” Step 4: Create Volume Group with new disk devices vgcreate s 4M <vg name> <list of all disks just created> example: vgcreate -s 4M vg_iris_db /dev/sd[h-k] Step 4: Create Logical Volume lvcreate n <lv name> -L <size of LV> -i <number of disks in volume group> -I 4MB <vg name> example: lvcreate -n lv_irisdb01 -L 1000G -i 4 -I 4M vg_iris_db Step 5: Create File System mkfs.xfs K <logical volume device> example: mkfs.xfs -K /dev/vg_iris_db/lv_irisdb01 Step 6: Mount File System edit /etc/fstab with following mount entries /dev/mapper/vg_iris_db-lv_irisdb01 /vol-iris/db xfs defaults 0 0 mount /vol-iris/db Using the above table, each of the InterSystems IRIS servers will have the following configuration with two disks for SYS, four disks for DB, two disks for primary journals and two disks for alternate journals. For growth LVM allows for expanding devices and logical volumes when needed without interruption. Consult with Linux documentation on best practices for ongoing management and expansion of LVM volumes. Note: The enablement of asynchronous IO for both the database and the write image journal files are highly recommend. See the following community article for details on enabling on Linux: https://community.intersystems.com/post/lvm-pe-striping-maximize-hyper-converged-storage-throughput Provisioning New with InterSystems IRIS is InterSystems Cloud Manager (ICM). ICM carries out many tasks and offers many options for provisioning InterSystems IRIS Data Platform. ICM is provided as a Docker image that includes everything for provisioning a robust GCP cloud-based solution. ICM currently support provisioning on the following platforms: Google Cloud Platform (GCP) Amazon Web Services including GovCloud (AWS / GovCloud) Microsoft Azure Resource Manager including Government (ARM / MAG) VMware vSphere (ESXi) ICM and Docker can run from either a desktop/laptop workstation or have a centralized dedicated modest “provisioning” server and centralized repository. The role of ICM in the application lifecycle is Define -> Provision -> Deploy -> Manage Details for installing and using ICM with Docker can be found here. NOTE: The use of ICM is not required for any cloud deployment. The traditional method of installation and deployment with tar-ball distributions is fully supported and available. However, ICM is recommended for ease of provisioning and management in cloud deployments. Container Monitoring ICM includes a basic monitoring facility using Weave Scope for container-based deployment. It is not deployed by default, and needs to be specified in the defaults file using the Monitor field. Details for monitoring, orchestration, and scheduling with ICM can be found here. An overview of Weave Scope and documentation can be found here. High Availability InterSystems database mirroring provides the highest level of availability in any cloud environment. There are options to provide some virtual machine resiliency directly at the instance level. Details of the various policies available in GCP can be found here. Earlier sections discussed how a cloud load balancer will provide automatic IP address failover for a Virtual IP (VIP-like) capability with database mirroring. The cloud load balancer uses the mirror_status.cxw health check status page mentioned earlier in the Internal Load Balancers section. There are two modes of database mirroring - synchronous with automatic failover and asynchronous mirroring. In this example, synchronous failover mirroring will be covered. The details of mirroring can he found here. The most basic mirroring configuration is a pair of failover mirror members in an arbiter-controlled configuration. The arbiter is placed in a third zone within the same region to protect from potential zone outages impacting both the arbiter and one of the mirror members. There are many ways mirroring can be setup specifically in the network configuration. In this example, we will use the network subnets defined previously in the Network Gateway and Subnet Definitions section of this document. Example IP address schemes will be provided in a following section and for the purpose of this section, only the network interfaces and designated subnets will be depicted. Disaster Recovery InterSystems database mirroring extends the capability of high available to also support disaster recovery to another GCP geographic region to support operational resiliency in the unlikely event of an entire GCP region going offline. How an application is to endure such outages depends on the recovery time objective (RTO) and recovery point objectives (RPO). These will provide the initial framework for the analysis required to design a proper disaster recovery plan. The following links provides a guide for the items to be considered when developing a disaster recovery plan for your application. https://cloud.google.com/solutions/designing-a-disaster-recovery-plan and https://cloud.google.com/solutions/disaster-recovery-cookbook Asynchronous Database Mirroring InterSystems IRIS Data Platform’s database mirroring provides robust capabilities for asynchronously replicating data between GCP zones and regions to help support the RTO and RPO goals of your disaster recovery plan. Details of async mirror members can be found here. Similar to the earlier high availability section, a cloud load balancer will provide automatic IP address failover for a Virtual IP (VIP-like) capability for DR asynchronous mirroring as well using the same mirror_status.cxw health check status page mentioned earlier in the Internal Load Balancers section. In this example, DR asynchronous failover mirroring will be covered along with the introduction of the GCP Global Load Balancing service to provide upstream systems and client workstations with a single anycast IP address regardless of which zone or region your InterSystems IRIS deployment is operating. One of the advances of GCP is the load balancer is a software defined global resource and not bound to a given region. This allows for the unique capability to leverage a single service across regions since it is not an instance or device-based solution. Details of GCP Global Load Balancing with Single Anycast IP can be found here. In the above example, the IP addresses of all three InterSystems IRIS instances are provided to the GCP Global Load Balancer, and it will only direct traffic to whichever mirror member is the acting primary mirror regardless of the zone or region it is located. Sharded Cluster InterSystems IRIS includes a comprehensive set of capabilities to scale your applications, which can be applied alone or in combination, depending on the nature of your workload and the specific performance challenges it faces. One of these, sharding, partitions both data and its associated cache across a number of servers, providing flexible, inexpensive performance scaling for queries and data ingestion while maximizing infrastructure value through highly efficient resource utilization. An InterSystems IRIS sharded cluster can provide significant performance benefits for a wide variety of applications, but especially for those with workloads that include one or more of the following: High-volume or high-speed data ingestion, or a combination. Relatively large data sets, queries that return large amounts of data, or both. Complex queries that do large amounts of data processing, such as those that scan a lot of data on disk or involve significant compute work. Each of these factors on its own influences the potential gain from sharding, but the benefit may be enhanced where they combine. For example, a combination of all three factors — large amounts of data ingested quickly, large data sets, and complex queries that retrieve and process a lot of data — makes many of today’s analytic workloads very good candidates for sharding. Note that these characteristics all have to do with data; the primary function of InterSystems IRIS sharding is to scale for data volume. However, a sharded cluster can also include features that scale for user volume, when workloads involving some or all of these data-related factors also experience a very high query volume from large numbers of users. Sharding can be combined with vertical scaling as well. Operational Overview The heart of the sharded architecture is the partitioning of data and its associated cache across a number of systems. A sharded cluster physically partitions large database tables horizontally — that is, by row — across multiple InterSystems IRIS instances, called data nodes, while allowing applications to transparently access these tables through any node and still see the whole dataset as one logical union. This architecture provides three advantages: Parallel processing: Queries are run in parallel on the data nodes, with the results merged, combined, and returned to the application as full query results by the node the application connected to, significantly enhancing execution speed in many cases. Partitioned caching: Each data node has its own cache, dedicated to the sharded table data partition it stores, rather than a single instance’s cache serving the entire data set, which greatly reduces the risk of overflowing the cache and forcing performance-degrading disk reads. Parallel loading: Data can be loaded onto the data nodes in parallel, reducing cache and disk contention between the ingestion workload and the query workload and improving the performance of both. Details of InterSystems IRIS sharded cluster can be found here. Elements of Sharding and Instance Types A sharded cluster consists of at least one data node and, if needed for specific performance or workload requirements, an optional number of compute nodes. These two node types offer simple building blocks presenting a simple, transparent, and efficient scaling model. Data Nodes Data nodes store data. At the physical level, sharded table[1] data is spread across all data nodes in the cluster and non-sharded table data is physically stored on the first data node only. This distinction is transparent to the user with the possible sole exception that the first node might have a slightly higher storage consumption than the others, but this difference is expected to become negligible as sharded table data would typically outweigh non-sharded table data by at least an order of magnitude. Sharded table data can be rebalanced across the cluster when needed, typically after adding new data nodes. This will move “buckets” of data between nodes to approximate an even distribution of data. At the logical level, non-sharded table data and the union of all sharded table data is visible from any node, so clients will see the whole dataset, regardless of which node they’re connecting to. Metadata and code are also shared across all data nodes. The basic architecture diagram for a sharded cluster simply consists of data nodes that appear uniform across the cluster. Client applications can connect to any node and will experience the data as if it were local. [1] For convenience, the term “sharded table data” is used throughout the document to represent “extent” data for any data model supporting sharding that is marked as sharded. The terms “non-sharded table data” and “non-sharded data” are used to represent data that is in a shardable extent not marked as such or for a data model that simply doesn’t support sharding yet. Data Nodes For advanced scenarios where low latencies are required, potentially at odds with a constant influx of data, compute nodes can be added to provide a transparent caching layer for servicing queries. Compute nodes cache data. Each compute node is associated with a data node for which it caches the corresponding sharded table data and, in addition to that, it also caches non-sharded table data as needed to satisfy queries. Because compute nodes don’t physically store any data and are meant to support query execution, their hardware profile can be tailored to suit those needs, for example by emphasizing memory and CPU and keeping storage to the bare minimum. Ingestion is forwarded to the data nodes, either directly by the driver (xDBC, Spark) or implicitly by the sharding manager code when “bare” application code runs on a compute node. Sharded Cluster Illustrations There are various combinations of deploying a sharded cluster. The following high-level diagrams are provided to illustrate the most common deployment models. These diagrams do not include the networking gateways and details and provide to focus only on the sharded cluster components. Basic Sharded Cluster The following diagram is the simplest sharded cluster with four data nodes deployed in a single region and in a single zone. A GCP Cloud Load Balancer is used to distribute client connections to any of the sharded cluster nodes. In this basic model, there is no resiliency or high availability provided beyond that of what GCP provides for a single virtual machine and its attached SSD persistent storage. Two separate network interface adapters are recommended to provide both network security isolation for the inbound client connections and also bandwidth isolation between the client traffic and the sharded cluster communications. Basic Sharded Cluster with High Availability The following diagram is the simplest sharded cluster with four mirrored data nodes deployed in a single region and splitting each node’s mirror between zones. A GCP Cloud Load Balancer is used to distribute client connections to any of the sharded cluster nodes. High availability is provided through the use of InterSystems database mirroring which will maintain a synchronously replicated mirror in a secondary zone within the region. Three separate network interface adapters are recommended to provide both network security isolation for the inbound client connections and bandwidth isolation between the client traffic, the sharded cluster communications, and the synchronous mirror traffic between the node pairs. This deployment model also introduces the mirror arbiter as described in an earlier section of this document. Sharded Cluster with Separate Compute Nodes The following diagram expands the sharded cluster for massive user/query concurrency with separate compute nodes and four data nodes. The Cloud Load Balancer server pool only contains the addresses of the compute nodes. Updates and data ingestion will continue to update directly to the data nodes as before to sustain ultra-low latency performance and avoid interference and congestion of resources between query/analytical workloads from real-time data ingestion. With this model the allocation of resources can be fine-tuned for scaling of compute/query and ingestion independently allowing for optimal resources where needed in a “just-in-time” and maintaining an economical yet simple solution instead of wasting resources unnecessarily just to scale compute or data. Compute Nodes lend themselves for a very straightforward use of GCP auto scale grouping (aka Autoscaling) to allow for automatic addition or deletion of instances from a managed instance group based on increased or decreased load. Autoscaling works by adding more instances to your instance group when there is more load (upscaling), and deleting instances when the need for instances is lowered (downscaling). Details of GCP Autoscaling can be found here. Autoscaling helps cloud-based applications gracefully handle increases in traffic and reduces cost when the need for resources is lower. Simply define the autoscaling policy and the autoscaler performs automatic scaling based on the measured load. Backup Operations There are multiple options available for backup operations. The following three options are viable for your GCP deployment with InterSystems IRIS. The first two options, detailed below, incorporate a snapshot type procedure which involves suspending database writes to disk prior to creating the snapshot and then resuming updates once the snapshot was successful. The following high-level steps are taken to create a clean backup using either of the snapshot methods: Pause writes to the database via database External Freeze API call. Create snapshots of the OS + data disks. Resume database writes via External Thaw API call. Backup facility archives to backup location Details of the External Freeze/Thaw APIs can be found here. Note: Sample scripts for backups are not included in this document, however periodically check for examples posted to the InterSystems Developer Community. www.community.intersystems.com The third option is InterSystems Online backup. This is an entry-level approach for smaller deployments with a very simple use case and interface. However, as databases increase in size, external backups with snapshot technology are recommended as a best practice with advantages including the backup of external files, faster restore times, and an enterprise-wide view of data and management tools. Additional steps such as integrity checks can be added on a periodic interval to ensure clean and consistent backup. The decision points on which option to use depends on the operational requirements and policies of your organization. InterSystems is available to discuss the various options in more detail. GCP Persistent Disk Snapshot Backup Backup operations can be achieved using GCP gcloud command-line API along with InterSystems ExternalFreeze/Thaw API capabilities. This allows for true 24x7 operational resiliency and assurance of clean regular backups. Details for managing and creating and automation GCP Persistent Disk Snapshots can be found here. Logical Volume Manager (LVM) Snapshots Alternatively, many of the third-party backup tools available on the market can be used by deploying individual backup agents within the VM itself and leveraging file-level backups in conjunction with Logical Volume Manager (LVM) snapshots. One of the major benefits to this model is having the ability to have file-level restores of either Windows or Linux based VMs. A couple of points to note with this solution, is since GCP and most other IaaS cloud providers do not provide tape media, all backup repositories are disk-based for short term archiving and have the ability to leverage blob or bucket type low cost storage for long-term retention (LTR). It is highly recommended if using this method to use a backup product that supports de-duplication technologies to make the most efficient use of disk-based backup repositories. Some examples of these backup products with cloud support include but is not limited to: Commvault, EMC Networker, HPE Data Protector, and Veritas Netbackup. Note: InterSystems does not validate or endorses one backup product over the other. The responsibility of choosing a backup management software is up to the individual customer. Online Backup For small deployments the built-in Online Backup facility is also a viable option as well. This InterSystems database online backup utility backs up data in database files by capturing all blocks in the databases then writes the output to a sequential file. This proprietary backup mechanism is designed to cause no downtime to users of the production system. Details of Online Backup can be found here. In GCP, after the online backup has finished, the backup output file and all other files in use by the system must be copied to some other storage location outside of that virtual machine instance. Bucket/Object storage is a good designation for this. There are two option for using a GCP Storage bucket. Use the gcloud scripting APIs directly to copy and manipulate the newly created online backup (and other non-database) files. Details can be found here. Mount a storage bucket as a file system and use it similarly as a persistent disk enough though Cloud Storage buckets are object storage. Details of mounting a Cloud Storage bucket using Cloud Storage FUSE can be found here.

Hi Community, We're pleased to invite you to the upcoming webinar in Hebrew: 👉 Introducing VS Code, and Moving from Studio in Hebrew 👈 🗓️ Date & time: July 25th, 3:00 PM IDT 🗣️ Speaker: @Tani.Frankel, Sales Engineer Manager In this session, we will review using VS Code for InterSystems-based development. It is aimed at beginners of VS Code, but will also cover some areas that might be beneficial for users who are already using VS Code. We will also cover some topics relevant to people moving from InterSystems Studio to VS Code. The session is relevant for users of Caché / Ensemble / InterSystems IRIS Data Platform / InterSystems IRIS for Health / HealthShare Health Connect. ➡️ Register today and enjoy! >>

InterSystems announces its fourth preview, as part of the developer preview program for the 2023.2 release. This release will include InterSystems IRIS and InterSystems IRIS for Health. Highlights Many updates and enhancements have been added in 2023.2 and there are also brand-new capabilities, such as Time-Aware Modeling, enhancements of Foreign Tables, and the ability to use Ready-Only Federated Tables. Note that some of these features or improvements may not be available in this current developer preview. Another important topic is the removal of the Private Web Server (PWS) from the installers. This feature has been announced since last year and will be removed from InterSystems installers, but they are still in this first preview. See this note in the documentation. --> If you are interested to try the installers without the PWS, please enroll in its EAP using this form, selecting the option "NoPWS". Additional information related to this EAP can be found here. Future preview releases are expected to be updated biweekly and we will add features as they are ready. Please share your feedback through the Developer Community so we can build a better product together. Initial documentation can be found at these links below. They will be updated over the next few weeks until launch is officially announced (General Availability - GA): InterSystems IRIS InterSystems IRIS for Health Availability and Package Information As usual, Continuous Delivery (CD) releases come with classic installation packages for all supported platforms, as well as container images in Docker container format. For a complete list, refer to the Supported Platforms document. Installation packages and preview keys are available from the WRC's preview download site or through the evaluation services website (use the flag "Show Preview Software" to get access to the 2023.2). Container images for both Enterprise and Community Editions of InterSystems IRIS and IRIS for Health and all corresponding components are available from the new InterSystems Container Registry web interface. For additional information about docker commands, please see this post: Announcing the InterSystems Container Registry web user interface. The build number for this developer preview is 2023.2.0.204.0. For a full list of the available images, please refer to the ICR documentation. Alternatively, tarball versions of all container images are available via the WRC's preview download site.

InterSystems announces its fifth preview, as part of the developer preview program for the 2023.2 release. This release will include InterSystems IRIS and InterSystems IRIS for Health. Highlights Many updates and enhancements have been added in 2023.2 and there are also brand-new capabilities, such as Time-Aware Modeling, and enhancements of Foreign Tables (but still as an experimental feature). Note that some of these features or improvements may not be available in this current developer preview. Another important topic is the removal of the Private Web Server (PWS) from the installers. This feature has been announced since last year and will be removed from InterSystems installers, but they are still in this first preview. See this note in the documentation. --> If you are interested to try the installers without the PWS, please enroll in its EAP using this form, selecting the option "NoPWS". Additional information related to this EAP can be found here. Future preview releases are expected to be updated biweekly and we will add features as they are ready. Please share your feedback through the Developer Community so we can build a better product together. Initial documentation can be found at these links below. They will be updated over the next few weeks until launch is officially announced (General Availability - GA): InterSystems IRIS InterSystems IRIS for Health Availability and Package Information As usual, Continuous Delivery (CD) releases come with classic installation packages for all supported platforms, as well as container images in Docker container format. For a complete list, refer to the Supported Platforms document. Installation packages and preview keys are available from the WRC's preview download site or through the evaluation services website (use the flag "Show Preview Software" to get access to the 2023.2). Container images for both Enterprise and Community Editions of InterSystems IRIS and IRIS for Health and all corresponding components are available from the new InterSystems Container Registry web interface. For additional information about docker commands, please see this post: Announcing the InterSystems Container Registry web user interface. The build number for this developer preview is 2023.2.0.210.0. For a full list of the available images, please refer to the ICR documentation. Alternatively, tarball versions of all container images are available via the WRC's preview download site.

As an IT and cloud team manager with 18 years of experience with InterSystems technologies, I recently led our team in the transformation of our traditional on-premises ERP system to a cloud-based solution. We embarked on deploying InterSystems IRIS within a Kubernetes environment on AWS EKS, aiming to achieve a scalable, performant, and secure system. Central to this endeavor was the utilization of the AWS Application Load Balancer (ALB) as our ingress controller. However, our challenge extended beyond the initial cluster and application deployment; we needed to establish an efficient and secure method to manage the various IRIS instances, particularly when employing mirroring for high availability. This post will focus on the centralized management solution we implemented to address this challenge. By leveraging the capabilities of AWS EKS and ALB, we developed a robust architecture that allowed us to effectively manage and monitor the IRIS cluster, ensuring seamless accessibility and maintaining the highest levels of security. In the following sections, we will delve into the technical details of our implementation, sharing the strategies and best practices we employed to overcome the complexities of managing a distributed IRIS environment on AWS EKS. Through this post, we aim to provide valuable insights and guidance to assist others facing similar challenges in their cloud migration journeys with InterSystems technologies. Configuration Summary Our configuration capitalized on the scalability of AWS EKS, the automation of the InterSystems Kubernetes Operator (IKO) 3.6, and the routing proficiency of AWS ALB. This combination provided a robust and agile environment for our ERP system's web services. Mirroring Configuration and Management Access We deployed mirrored IRIS data servers to ensure high availability. These servers, alongside a single application server, were each equipped with a Web Gateway sidecar pod. Establishing secure access to these management portals was paramount, achieved by meticulous network and service configuration. Detailed Configuration Steps Initial Deployment with IKO: We leveraged IKO 3.6, we deployed the IRIS instances, ensuring they adhered to our high-availability requirements. Web Gateway Management Configuration: We create server access profiles within the Web Gateway Management interface. These profiles, named data00 and data01, were crucial in establishing direct and secure connectivity to the respective Web Gateway sidecar pods associated with each IRIS data server. To achieve precise routing of incoming traffic to the appropriate Web Gateway, we utilized the DNS pod names of the IRIS data servers. By configuring the server access profiles with the fully qualified DNS pod names, such as iris-svc.app.data-0.svc.cluster.local and iris-svc.app.data-1.svc.cluster.local, we ensured that requests were accurately directed to the designated Web Gateway sidecar pods. https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls?KEY=GCGI_config_serv IRIS Terminal Commands: To align the CSP settings with the newly created server profiles, we executed the following commands in the IRIS terminal: d $System.CSP.SetConfig("CSPConfigName","data00") # on data00 d $System.CSP.SetConfig("CSPConfigName","data01") # on data01 https://docs.intersystems.com/healthconnectlatest/csp/docbook/DocBook.UI.Page.cls?KEY=GCGI_remote_csp NGINX Configuration: The NGINX configuration was updated to respond to /data00 and /data01 paths, followed by creating Kubernetes services and ingress resources that interfaced with the AWS ALB, completing our secure and unified access solution. Creating Kubernetes Services: I initiated the setup by creating Kubernetes services for the IRIS data servers and the SAM: Ingress Resource Definition: Next, I defined the ingress resources, which route traffic to the appropriate paths using annotations to secure and manage access. Explanations for the Annotations in the Ingress YAML Configuration: alb.ingress.kubernetes.io/scheme: internal Specifies that the Application Load Balancer should be internal, not accessible from the internet. This ensures that the ALB is only reachable within the private network and not exposed publicly. alb.ingress.kubernetes.io/subnets: subnet-internal, subnet-internal Specifies the subnets where the Application Load Balancer should be provisioned. In this case, the ALB will be deployed in the specified internal subnets, ensuring it is not accessible from the public internet. alb.ingress.kubernetes.io/target-type: ip Specifies that the target type for the Application Load Balancer should be IP-based. This means that the ALB will route traffic directly to the IP addresses of the pods, rather than using instance IDs or other target types. alb.ingress.kubernetes.io/target-group-attributes: stickiness.enabled=true Enables sticky sessions (session affinity) for the target group. When enabled, the ALB will ensure that requests from the same client are consistently routed to the same target pod, maintaining session persistence. alb.ingress.kubernetes.io/listen-ports: '[{"HTTPS": 443}]' Specifies the ports and protocols that the Application Load Balancer should listen on. In this case, the ALB is configured to listen for HTTPS traffic on port 443. alb.ingress.kubernetes.io/certificate-arn: arn:aws:acm:il- Specifies the Amazon Resource Name (ARN) of the SSL/TLS certificate to use for HTTPS traffic. The ARN points to a certificate stored in AWS Certificate Manager (ACM), which will be used to terminate SSL/TLS connections at the ALB. These annotations provide fine-grained control over the behavior and configuration of the AWS Application Load Balancer when used as an ingress controller in a Kubernetes cluster. They allow you to customize the ALB's networking, security, and routing settings to suit your specific requirements. After configuring the NGINX with location settings to respond to the paths for our data servers, the final step was to extend this setup to include the SAM by defining its service and adding the route in the ingress file. Security Considerations: We meticulously aligned our approach with cloud security best practices, particularly the principle of least privilege, ensuring that only necessary access rights are granted to perform a task. DATA00: DATA01: SAM: Conclusion: This article shared our journey of migrating our application to the cloud using InterSystems IRIS on AWS EKS, focusing on creating a centralized, accessible, and secure management solution for the IRIS cluster. By leveraging security best practices and innovative approaches, we achieved a scalable and highly available architecture. We hope that the insights and techniques shared in this article prove valuable to those embarking on their own cloud migration projects with InterSystems IRIS. If you apply these concepts to your work, we'd be interested to learn about your experiences and any lessons you discover throughout the process I found this extremely useful, Thank you. Amazing architecture and sofisticated system. The use of InterSystem IRIS infrastructure is a result of hard word and complicated integration with the system. Thanks for sharing

Hi Community, Enjoy the new video on InterSystems Developers YouTube: ⏯ Rapidly Create and Deploy Secure REST Services on InterSystems IRIS @ Global Summit 2024 Learn about isc-rest, an open source package for defining REST APIs for CRUD operations, class queries, and business logic. We'll cover the basic concepts and patterns and talk about the success TriFour has had using it in their products. Presenters:🗣 @Timothy.Leavitt, Development Manager, InterSystems🗣 @Gerrit.Henning1669, CEO, TriFour🗣 @Stephan.duPlooy7271, CTO, TriFour Enjoy watching, and look forward to more videos!👍

When using standard SQL or the object layer in InterSystems IRIS, metadata consistency is usually maintained through built-in validation and type enforcement. However, legacy systems that bypass these layers—directly accessing globals—can introduce subtle and serious inconsistencies. Understanding how drivers behave in these edge cases is crucial for diagnosing legacy data issues and ensuring application reliability.The DATATYPE_SAMPLE database is designed to help analyze error scenarios where column values do not conform to the data types or constraints defined in the metadata. The goal is to evaluate how InterSystems IRIS and its drivers (JDBC, ODBC, .NET) and different tools behave when such inconsistencies occur. In this post, I’ll focus on the JDBC driver. What's the Problem? Some legacy applications write directly to globals. If a relational model (created via CREATE TABLE or manually defined using a global mapping) is used to expose this data, the mapping defines the underlying values conform to the declared metadata for each column. When this assumption is broken, different types of problems may occur: Access Failure: A value cannot be read at all, and an exception is thrown when the driver tries to access it. Silent Corruption: The value is read successfully but does not match the expected metadata. Undetected Mutation: The value is read and appears valid, but was silently altered by the driver to fit the metadata, making the inconsistency hard to detect. Simulating the Behavior To demonstrate these scenarios, I created the DATATYPE_SAMPLE database, available on the InterSystems Open Exchange:🔗 Package page🔗 GitHub repo The table used for the demonstration: CREATE TABLE SQLUser.Employee ( ID BIGINT NOT NULL AUTO_INCREMENT, Age INTEGER, Company BIGINT, DOB DATE, FavoriteColors VARCHAR(4096), Name VARCHAR(50) NOT NULL, Notes LONGVARCHAR, Picture LONGVARBINARY, SSN VARCHAR(50) NOT NULL, Salary INTEGER, Spouse BIGINT, Title VARCHAR(50), Home_City VARCHAR(80), Home_State VARCHAR(2), Home_Street VARCHAR(80), Home_Zip VARCHAR(5), Office_City VARCHAR(80), Office_State VARCHAR(2), Office_Street VARCHAR(80), Office_Zip VARCHAR(5) ); Example 1: Access Failure To simulate an inconsistency, I injected invalid values into the DOB (Date of Birth\Datatype DATE) column using direct global access. Specifically, the rows with primary keys 101, 180, 181, 182, 183, 184, and 185 were populated with values that do not represent valid dates.The values looks like this now: As you can see, a string was appended to the end of a $H (Horolog) value. According to the table's metadata, this column is expected to contain a date—but the stored value clearly isn't one. So what happens when you try to read this data? Well, it depends on the tool you're using. I tested a few different tools to compare how they handle this kind of inconsistency. 1) SquirrelSQL (SQuirreL SQL Client Home Page)When SquirrelSQL attempts to access the data, an error occurs. It tries to read all rows and columns, and any cell that contains invalid data is simply marked as "ERROR". Unfortunately, I couldn't find any additional details or error messages explaining the cause. 2) SQLWorkbench/J (SQL Workbench/J - Home)SQL Workbench/J stops processing the result set as soon as it encounters the first invalid cell. It displays an error message like "Invalid date", but unfortunately, it doesn't provide any information about which row caused the issue. 3) DBVisualizer (dbvis) & DBeaver (dbeaver) DBVisualizer and DBeaver behave similarly. Both tools continue reading the result set and provide detailed error messages for each affected cell. This makes it easy to identify the corresponding row that caused the issue. 4) SQL DATA LENS (SQL Data Lens - a powerful tool for InterSystems IRIS and Caché) With the latest release of SQL DATA LENS, you get detailed information about the error, the affected row, and the actual database value. As shown in the screenshot, the internal value for the first row in columns DOB is "39146<Ruined>", which cannot be cast to a valid DATE. SQL DATA LENS also allows you to configure whether result processing should stop at the first erroneous cell or continue reading to retrieve all available data. The next part of this article will shows details about: Silent Corruption: The value is read successfully but does not match the expected metadata. Undetected Mutation: The value is read and appears valid, but was silently altered by the driver to fit the metadata, making the inconsistency hard to detect. Andreas

In this series of articles, I'd like to present and discuss several possible approaches toward software development with InterSystems technologies and GitLab. I will cover such topics as: First article Git basics, why a high-level understanding of Git concepts is important for modern software development, How Git can be used to develop software (Git flows) Second article GitLab Workflow - a complete software life cycle process - from idea to user feedback Continuous Delivery - software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time. It aims at building, testing, and releasing software faster and more frequently. Third article GitLab installation and configuration Connecting your environments to GitLab Fourth article Continuous delivery configuration Fifth article Containers and how (and why) they can be used. Sixth article Main components for a continuous delivery pipeline with containers How they all work together. Seventh article Continuous delivery configuration with containers Eighth article Continuous delivery configuration with InterSystems Cloud Manager Ninth article Container architecture Tenth article CI/CD for Configuration and Data Eleventh article Interoperability and CI/CD Twelfth article Dynamic Inactivity Timeouts In this series of articles, I covered general approaches to the Continuous Delivery. It is an extremely broad topic and this series of articles should be seen more as a collection of recipes rather than something definitive. If you want to automate building, testing and delivery of your application Continuous Delivery in general and GitLab in particular is the way to go. Continuous Delivery and containers allows you to customize your workflow as you need it.

Your may not realize it, but your InterSystems Login Account can be used to access a very wide array of InterSystems services to help you learn and use InterSystems IRIS and other InterSystems technologies more effectively. Continue reading to learn more about how to unlock new technical knowledge and tools using your InterSystems Login account. Also - after reading, please participate in the Poll at the bottom, so we can see how this article was useful to you! What is an InterSystems Login Account? An InterSystems Login account is used to access various online services which serve InterSystems prospects, partners, and customers. It is a single set of credentials used across 15+ externally facing applications. Some applications (like the WRC, or iService) require specific activation for access to be granted by the account. Chances are there is are resources which will help you but you didn't know about - make sure to read about all of the options and try out a new tool to help up your technical game!! Application Catalog You can view all services available to you with your InterSystems Login account by visiting that InterSystems Application Catalog, located at: https://Login.InterSystems.com. This will list only those applications or services to which you currently have access. It remembers your most frequently used applications and lists them at the top for your convenience. Make sure to Bookmark the page for easy access to all of these tools in your InterSystems Login Account toolbox! Application Details Now it's time to get into the details of the individual applications and how they can help you as a developer working with InterSystems technologies! Read on and try to find a new application to leverage for the first time in order to improve your efficiency and skills as a developer.... Getting Started - gettingstarted.intersystems.com Audience Anyone wishing to explore using InterSystems IRIS® data platform Description Learn how to build data-intensive, mission-critical applications fast with InterSystems IRIS. Work through videos and tutorials leveraging SQL, Java, C#/.Net, Node.js, Python, or InterSystems ObjectScript. Use a free, cloud-based, in-browser Sandbox -- IRIS+IDE+Web Terminal—to work through tutorials. How it helps Up Your Technical Game Quickly get oriented with InterSystems technology and see it in action with real working code and examples! Explore the use of other popular programming languages with InterSystems IRIS. Online Learning - learning.intersystems.com Audience All users and potential users of InterSystems products Description Self-paced materials to help you build and support the world's most important applications: Hands-on exercises Videos Online Courses Learning Paths How it helps Up Your Technical Game Learn, learn, learn!! Nothing will help you become a more effective developer faster than following a skilled technical trainer as they walk you through new concepts to use in your InterSystems IRIS projects! Documentation - docs.intersystems.com Audience All users and potential users of InterSystems products Description Documentation for all versions of our products Links where needed to external documentation All recent content, is fed through our new search engine. Search page lets you filter by product, version, and other facets. Certain docs require authorization (via InterSystems Login account): AtScale docs available to Adaptive Analytics customers HealthShare docs are available to HealthShare users Make sure to make use of the new dynamic Upgrade Impact Checklist within the Docs server! How it helps Up Your Technical Game Quickly make use of class reference material and API documentation. Find example code. Read detailed usage documentation for parts of InterSystems IRIS into which you need a deeper dive. Request additional detail or report issues direct from within the documentation pages via the "Feedback" feature. Evaluation - evaluation.intersystems.com Audience Those wishing to download InterSystems software or licenses for evaluation or development use Description Downloads of InterSystems IRIS and InterSystems IRIS for Health. Anybody can download Community Edition kits. Existing customers can also request a powerful license to evaluate enterprise features. Preview versions are available pre-release. Early Access Program packages allow people to provide feedback on future products and features. How it helps Up Your Technical Game Try out Preview versions of software to see how new features can help to accelerate your development. Test run Enterprise features by requesting an evaluation license. Make sure all developers in your organization have the latest version of InterSystems IRIS installed on their machines. Provide feedback to InterSystems Product Management about Early Access Features to ensure that they will meet your team's needs once they are fully released. Developer Community - community.intersystems.com Audience Anyone working with InterSystems technology (InterSystems employees, customers, partners, and prospects) Description Monitor announcements related to InterSystems products and services. Find articles on a variety of technical topics. Ask questions and get answers from the community. Explore job postings or developers available for hire. Participate in competitions featuring $1000’s in cash prizes. Stay up to date concerning all things InterSystems! How it helps Up Your Technical Game With access to the leading global experts on InterSystems technology, you can learn from the best and stay engaged with the hottest questions, trends and topics. Automatically get updates in your inbox on new products, releases, and Early Access Program opportunities. Get help from peers to answer your questions and move past blockers. Have enriching discussions with InterSystems Product Managers and Product Developers - learn from the source! Push your skills to the next level by sharing technical solutions and sharing code and gaining from feedback from others. InterSystems Ideas - ideas.intersystems.com Audience Those looking to share ideas for improving InterSystems technology. Description Post ideas on how to make InterSystems technology better. Read existing reviews and up-vote or engage in discussions. InterSystems will take the most popular ideas into account for future product roadmaps. How it helps Up Your Technical Game See your ideas and needs turned into a reality within InterSystems products or open source libraries. Become familiar with the ideas of your peers and learn to use InterSystems products in new ways. Implement ideas suggested by others, new exploring parts of InterSystems technology. Global Masters - globalmasters.intersystems.com Audience Those wishing to advocate for InterSystems technology and earn badges and swag Description Gamification platform designed for developers to learn, stay up-to-date and get recognition for contributions via interactive content. Users receive points and badges for: Engagement on the Developer Community Engagement on the Open Exchange Publishing posts to social media about InterSystems products and technologies Trade in points for InterSystems swag or free training How it helps Up Your Technical Game Challenges bring to your attention articles or videos which you may have missed on the Developer Community, Learning site or YouTube channel - constantly learning new things to apply to your projects! Open Exchange - openexchange.intersystems.com Audience Developers seeking to publish or make use of reusable software packages and tools Description Developer tools and packages built with InterSystems data platforms and products. Packages are published under a variety of software licenses (mostly open source). Integrated with GitHub for package versioning, discussions, and bug tracking. Read and submit reviews and find the most popular packages. Developers can submit issues and make improvements to packages via GitHub pull requests to help push community software forward. Developers can see statistics of traffic and downloads of the packages they published How it helps Up Your Technical Game Don't reinvent the wheel! Use open source packages created and maintained by the InterSystems Community to solve generic problems, leaving you to focus on developing solutions needed specifically by your business. Contributing to open source packages is a great way to receive constructive feedback on your work and refine your development patterns. Becoming a respected contributor to open source projects is a great way to see demand increase for your skills and insights. WRC - wrc.intersystems.com Audience Issue tracking system for all customer reported problems on InterSystems IRIS and InterSystems HealthShare. Customers with SUTA can work directly with the application. Description Worldwide Response Center application (aka “WRC Direct”). Issue tracking system for all customer reported problems. Open new requests. See all investigative actions and add information and comments about a request. See statistical information about your support call history. Close requests and provide feedback about the support process. Review ad-hoc patch files. Monitor software change requests. Download current product and client software releases. How it helps Up Your Technical Game InterSystems Support Engineers can help you get past any technical blocker you have concerning development or systems management with InterSystems products. Report bugs to ensure that issues are fixed in future releases. iService - iservice.intersystems.com Audience Customers requiring support under an SLA agreement Description A support ticketing platform for our healthcare, cloud and hosted customers. Allows for rule driven service-level agreement (SLA) compliance calculation and reporting. Provides advanced facet search and export functionality. Incorporates a full Clinical Safety management system. How it helps Up Your Technical Game InterSystems Support Engineers can help you get past any technical blocker you have concerning development or systems management with InterSystems healthcare or cloud products. Report bugs to ensure that issues are fixed in future releases. ICR - containers.intersystems.com Audience Anyone who wants to use InterSystems containers Description InterSystems Container Registry A programmatically accessible container registry and web UI for browsing. Community Edition containers available to everyone. Commercial versions of InterSystems IRIS and InterSystems IRIS for Health available for supported customers. Generate tokens to use in CICD pipelines for automatically fetching containers. How it helps Up Your Technical Game Increase the maturity of your SDLC by moving to container-based CICD pipelines for your development, testing and deployment! Partner Directory - partner.intersystems.com Audience Those looking to find an InterSystems partner or partner’s product Partners looking to advertise their software and services Description Search for all types of InterSystems partners: Implementation Partners Solution Partners Technology Partners Cloud Partner Existing partners can manage their service and software listings. How it helps Up Your Technical Game Bring in certified experts on a contract basis to learn from them on your projects. License enterprise solutions based on InterSystems technology so you don't have to build everything from scratch. Bring your products and services to a wider audience, increasing demand and requiring you to increase your ability to deliver! CCR - ccr.intersystems.com Audience Select organizations managing changes made to an InterSystems implementation (employees, partners and end users) Description Change Control Record Custom workflow application built on our own technology to track all customizations to InterSystems healthcare products installed around the world. Versioning and deployment of onsite custom code and configuration changes. Multiple Tiers and workflow configuration options. Very adaptable to meet the specific needs of the phase of the project How it helps Up Your Technical Game For teams authorized for its use, find and reuse code or implementation plans within your organization, preventing having to solve the same problem multiple times. Resolve issues in production much more quickly, leaving more time for development work. Client Connection - client.intersystems.com Audience Available to any TrakCare clients Description InterSystems Client Connection is a collaboration and knowledge-sharing platform for TrakCare clients. Online community for TrakCare clients to build more, better, closer connections. On Client Connection you will find the following: TrakCare news and events TrakCare release materials, e.g. release documentation and preview videos Access to the most up-to-date product guides. Support materials to grow personal knowledge. Discussion forums to leverage peer expertise. How it helps Up Your Technical Game Technical and Application Specialists at TrakCare sites can share questions and knowledge quickly - connecting with other users worldwide. Faster answers means more time to build solutions! Online Ordering - store.intersystems.com Audience Operations users at selected Application partners/end-users Description Allow customers to pick different products according to their contracts and create new orders. Allow customers to upgrade/trade-in existing orders. Submit orders to InterSystems Customer Operations to process them for delivery and invoicing. Allow customers to migrate existing licenses to InterSystems IRIS. How it helps Up Your Technical Game Honestly, it doesn't! It's a tool used by operations personnel and not technical users, but it is listed here for completeness since access is controlled via the InterSystems Login Account ;) Other Things to Know About your InterSystems Login Account Here are a few more useful facts about InterSystems Login Accounts... How to Create a Login Account Users can make their own account by clicking "Create Account" on any InterSystems public-facing application, including: https://evaluation.intersystems.com https://community.intersystems.com https://learning.intersystems.com Alternatively, the InterSystems FRC (First Response Center) will create a Login Account for supported customers the first time they need to access the Worldwide Response Center (WRC) or iService (or supported customers can also create accounts for their colleagues). Before using an account, a user must accept the Terms and Conditions, either during the self-registration process or the first time they log in. Alternative Login Options Certain applications allow login with Google or GitHub: Developer Community Open Exchange Global Masters This is the same InterSystems Login Account, but with authentication by Google or GitHub. Account Profile If you go to https://Login.InterSystems.com and authenticate, you will be able to access Options > Profile and make basic changes to your account. Email can be changed via Options > Change Email. Resolving Login Account Issues Issues with InterSystems Login Accounts should be directed to Support@InterSystems.com. Please include: Username used for attempted login Email Browser type and version Specific error messages and/or screenshots Time and date the error was received Please remember to vote in the poll once you read the article! Feel free to ask questions here about apps that may be new to you. Best part for me is having one spot instead of trying to remember the links to all the pieces. Thanks @Mindy.Caldwell - that is the goal! Glad you find it to be useful :) 💡 This article is considered as InterSystems Data Platform Best Practice.

Hi Developers! Here are the technology bonuses for the InterSystems FHIR and Digital Health Interoperability Contest 2025 that will give you extra points in the voting: InterSystems FHIR usage - 3 Digital Health Interoperability - 4 Vector Search - 3 LLM AI or LangChain usage: Chat GPT, Gemini and others - 3 Embedded Python - 2 Docker container usage - 2 IPM Package deployment - 2 Online Demo - 2 Implement InterSystems Community Idea - 4 Find a bug in InterSystems FHIR server - 2 Find a bug in InterSystems Interoperability - 2 New First Article on Developer Community - 2 New Second Article on Developer Community - 1 First Time Contribution - 3 Video on YouTube - 3 See the details below. InterSystems FHIR usage - 3 points Implement InterSystems FHIR server in your application either as a standalone cloud FHIR server or as a component of InterSystems IRIS for Health and collect 3 bonus points! Digital Health Interoperability - 4 points Collect 4 bonus points if your application is a healthcare interoperability solution that uses InterSystems Interoperability to transfer or/and transform healthcare data via messages or it uses healthcare format data transformation. Here are a couple of examples: one, two, three. Vector Search - 3 points Starting from the 2024.1 release, InterSystems IRIS contains a new technology vector search that allows building vectors over InterSystems IRIS data and performing a search of already indexed vectors. Use it in your solution and collect 3 bonus points. Here is the demo project that leverages it. LLM AI or LangChain usage: Chat GPT, Bard, and others - 2 points Collect 3 bonus expert points for building a solution that uses LangChain libs or Large Language Models (LLM) such as ChatGPT, Bard and other AI engines like PaLM, LLaMA, and more. AutoGPT usage count,s too. A few examples already could be found in Open Exchange: iris-openai, chatGPT telegram bot, rag-demo. Here is an article with langchain usage example. Embedded Python - 2 points Use Embedded Python in your application and collect 2 extra points. Base template, example application with Interoperability. Docker container usage - 2 points The application gets a 'Docker container' bonus if it uses InterSystems IRIS running in a docker container. Here is the simplest template to start from. ZPM Package deployment - 2 points You can collect the bonus if you build and publish the ZPM(InterSystems Package Manager) package for your Full-Stack application so it could be deployed with: zpm "install your-multi-model-solution" command on IRIS with ZPM client installed. ZPM client. Documentation. Online Demo of your project - 2 pointsCollect 2 more bonus points if you provision your project to the cloud as an online demo at any public hosting. Implement Community Opportunity Idea - 4 points Implement any idea from the InterSystems Community Ideas portal which has the "Community Opportunity" status. This will give you 4 additional bonus points. Find a bug in InterSystems Digital Health Interoperability - 2 pointsWe want the broader adoption of InterSystems Interoperability engine so we encourage you to report the bugs you will face during the development of your interoperability application with IRIS in order to fix it. Please submit the bug here in a form of issue and how to reproduce it. You can collect 2 bonus points for the first reproducible bug. Find a bug in InterSystems FHIR Server - 2 pointsWe want the broader adoption of InterSystems FHIR, so we encourage you to report the bugs you will face during the development of your FHIR application in order to fix them. Please submit the bug here in a form of an issue and how to reproduce it. You can collect 2 bonus points for the first reproducible bug. New First Article on Developer Community - 2 points Write a brand new article on Developer Community that describes the features of your project and how to work with it. Collect 2 points for the article. New Second Article on Developer Community - 1 point You can collect one more bonus point for the second new article or the translation regarding the application. The 3rd and more will not bring more points but the attention will all be yours. First-Time Contribution - 3 points Collect 3 bonus points if you participate in InterSystems Open Exchange contests for the first time! Video on YouTube - 3 points Make new YouTube videos that demonstrate your product in action and collect 3 bonus points for each. The list of bonuses is subject to change. Stay tuned! Good luck in the competition!

Okay, it's officially going to be an outstanding Developer Zone this year at InterSystems READY 2025!Tech Exchange — a special space filled with developer-tailored content — is back this year, better than ever! Here's what to explore at Tech Exchange: ✅ More than 40 short tech demos with Q&A✅ Developer Ecosystem Booth: Meet and chat with the Developer Relations team — the people behind the Developer Community, Global Masters, Ideas Portal, Open Exchange, Programming Contests, and many other activities we run for developers! Try your luck at the Wheel of Fortune Snap a photo at our booth for a secret project (we’ll reveal more at the booth!) and earn Global Masters points🍪 Did you know there will be a barista in the room? Well, our table has cookies! Help us clear the cookies — yes, the edible kind!As always, learn about everything we offer to support developers — including our new AI-powered Developer Community tool (get faster answers and debug errors more easily), programming and writing contests with prizes, and tons of great resources!✅ Learning Services booth with one-on-one training sessions and learning plan consultations. Learn more here ✅ User Experience booth✅ Request a 1-on-1 meeting with InterSystems product experts and developers Dedicated tables with InterSystems experts that will be ready to answer your questions:✅ FHIR & Interoperability tableAttend the FHIR Table at Tech Exchange to Learn About New Bulk FHIR Features: Population-level analytics across patient cohorts and EHRs Building high-quality research datasets with FHIR Extracting clinical measures, operational analytics, and care quality metrics from health systems using FHIR How SMART on FHIR and FHIR Bulk Access support third-party application development and innovation ✅ Python table of Mumpsters and Pythonistas Mumpsters, you have question around Python? Python developpers you have question around IRIS ? come to the Python Table! Meet with our experts to discuss all things Python and IRIS. We will be happy to answer your questions, share best practices, and help you get the most out of your Python experience with IRIS. 🐍 Topics: Interoperability on Python (IoP) Remote debugging Deep Dive in low level APIs Best practices How to start with Python and IRIS Virtual Environment Support ✅ Got SQL Questions? Stop by the SQL Table!Meet InterSystems experts and get quick answers on IRIS SQL, vector search, query tuning, data modeling, and more. Topics: IRIS SQL fundamentals Vector search and foreign tables Data modeling and indexing strategies Query optimization and error investigation Document storage and sharding techniques ✅ Visit the Supply Chain Table Stop by for one-on-one conversations and live demos covering the latest in InterSystems supply chain solutions. Topics: Overview of InterSystems supply chain solutions and strategy Deep dive into our key products: InterSystems Supply Chain OrchestratorInterSystems Data Fabric Studio with supply chain module Real-world client use cases and implementation stories Live demos featuring: Intelligent supply chain for smarter healthcare Empowering enterprise applications through a supply chain data gateway Decision intelligence with Supply Chain Orchestrator Generative AI Co-Pilot for supply chain workflows What: Tech Exchange @ InterSystems READY 2025Where: Bonnet Creek IX As always, we have a number of Global Masters activities - both fun and informational - to keep you updated on the event while you also earning the points. Check them out under "InterSystems READY" tag. 👏