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Hi,The above pipeline reads the CSV files uploaded into an S3 bucket for new contact file uploads. Based on the new contacts available as a CSV, it converts them into a JSON. But the critical part here is that it merges each batches (each batch size is 1000) into a single JSON of multiple records and finally make a single HTTP POST call to one of our internal system to carry out a CONTACT update call.
During SDC startup by default SDC try connecting to repository manifest URL 2022-09-12T21:38:54,103 [user:] [pipeline:] [runner:] [thread:ManifestFetcher] [stage:]INFO StageLibraryUtil - Reading from Repository Manifest URL: http://archives.streamsets.com/datacollector/5.0.0/tarball/repository.manifest.jsonNow, if your SDC doesn’t have internet connectivity it will failed with the below error, However, SDC will start without any issue 2022-09-12T21:38:56,714 [user:] [pipeline:] [runner:] [thread:ManifestFetcher] [stage:] ERROR StageLibraryUtil - Failed to read repository manifest jsonjavax.ws.rs.ProcessingException: java.net.SocketTimeoutException: connect timed out at org.glassfish.jersey.client.internal.HttpUrlConnector.apply(HttpUrlConnector.java:287) ~[jersey-client-2.25.1.jar:?] at org.glassfish.jersey.client.ClientRuntime.invoke(ClientRuntime.java:252) ~[jersey-client-2.25.1.jar:?] at org.glassfish.jersey.client.JerseyInvocation$1.call(JerseyInvocation.java:
When a data collector pipeline is in STARTING state it will validate each stage, create required clients and also do initialization.Each stage has init() method which initializes the objects needed for stages to run. Validation runs static operations. For example, running the query given in JDBC origin stages with limited results to validate the query, its schema, offset column etc. Do note that validation is specific to each stage.If you notice any slowness in STARTING to RUNNING stage, it is possible that validation is taking sometime and it usually happens when the datasets in configured query are larger. This is an example of slowness for JDBC or Relational DB stages. If the slowness is unacceptable you might consider disabling the query validation.
Connecting to Microsoft SQL Server from SDC v5.1 is failing "unable to find valid certification path to requested target"
Environment:Streamsets Data Collector 5.1. Producer or consumer Library for Microsoft SQL Server. Issue: The pipeline starts failing with following messages when try to fetch or produce the data to Microsoft SQL Server.This happens on data collector is 5.1 but the same pipeline is working fine on 4.x versions.com.microsoft.sqlserver.jdbc.SQLServerException: The driver could not establish a secure connection to SQL Server by using Secure Sockets Layer (SSL) encryption. Error: "sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target". ClientConnectionId:af15a6cf-f28d-4e21-adb3-141826becad0 at com.microsoft.sqlserver.jdbc.SQLServerConnection.terminate(SQLServerConnection.java:3680) ~[mssql-jdbc-10.2.1.jre8.jar:?] at com.microsoft.sqlserver.jdbc.TDSChannel.enableSSL(IOBuffer.java:2113) ~[mssql-jdbc-10.2.1.jre8.jar:?] at com.microsoft.sqlserver.jdbc.SQLServer
Hello community,My pipeline is being upgraded to ingest more fields and I want to write these new fields to the existing table in Hadoop. I have searched for the metadata processor but it does not exist and I have enabled data drift. None of them are able to write new columns, which means I have to write an ALTER TABLE statement to capture these new fields. With time we will get further requirements to expand our tables and introduce new fields. Is StreamSets able to write new fields automatically?Kind regards,Nick
When Websocket Tunneling is enabled, you won’t be able to download support bundle through dataops platform UI. We are shown below message when we try to access it from Engine > Support Bundle page.The support bundle allows you to generate an archive file with the information required to troubleshoot various issues with the engine. To download a support bundle, you must set up direct engine access and turn off Websocket Tunneling in your Browser Settings.Follow below steps to download support bundle:Generate an API Credential from DataOpsDataOps Platform -> Manage -> API Credentials -> Add New API Credential -> (Note Credential ID and Token)Run the following commands on the engine(SDC or Transformer) (replace CRED_ID and CRED_TOKEN values) where the relevant pipeline has been run.export CRED_ID=<xxxxxxxx>export CRED_TOKEN=<xxxxxxxx>curl -X GET '<URL-of-the-engine>/rest/v1/system/bundle/generate?generators=SdcInfoContentGenerator,PipelineContentGenerator,
This pipeline is designed to orchestrate initial bulk load to change data capture workload from on-prem Oracle database to Snowflake Data Cloud. The pipeline takes into consideration the completion status of the initial bulk load job before proceeding to the next (CDC) step and also sends success/failure notifications. The pipeline also takes advantage of the platform’s event framework to automatically stop the pipeline when there’s no more data to be ingested.
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HI team, I am having 6 API’s where I have to build 3 pipelines for each i.e., API-KAFKA, KAFKA-FILESYSTEM and FS-Target table. Like this For 6 API’s I have to build and execute 18 pipelines (6*3). Could anyone suggest me that is there any approach which reduces the number of pipelines ( to push all 6 APIs data into 1 Kafka topic in 1st pipeline, after that I will generate data file from Kafka to FS and FS to target table).FYI- my target table is same for all APIs.
Hello streamset community, I configured xmx and xms with 35GB, like this: SDC_JAVA_OPTS="-Xmx35840m -Xms35840m means that 35G has been configured, but querying the service status with "systemctl status sdc" command, the memory value exceeds 37G, as shown below: why does this occur? does the memory value that appears with the "systemctl status sdc" command have a different conversion or does this memory add to another type of memory?
The streamsets pipeline fails on a regular basis with the below error. Please advise if you ran into this issue and the resolution. We have increased Heap size couple of times but not helping.“ERROR A JVM error occurred while running the pipeline, java.lang.OutOfMemoryError: Java heap space”
Trying to build data pipeline for Azure SQL Server DB (CDC) as source and Azure Data bricks (Delta tables) as destinationI have referred data pipeline sample fromhttps://github.com/streamsets/pipeline-library/tree/master/datacollector/sample-pipelines/pipelines/SQLServer%20CDC%20to%20Delta%20Lake Getting below error for few records in Schema preview as-well:DELTA_LAKE_34 - Databricks Delta Lake load request failed: 'DELTA_LAKE_32 - Could not copy staged file 'sdc-4a076fce-7a73-45ba-8dd7-29e58848cf23.csv': java.sql.SQLException: [Simba][SparkJDBCDriver](500051) ERROR processing query/statement. Error Code: 0, SQL state: org.apache.hive.service.cli.HiveSQLException: Error running query: org.apache.spark.sql.AnalysisException: Unable to infer schema for CSV. It must be specified manually. Note : On Preview/Draft Run → Pipeline is able to capture changes from Source DB, successfully created files in stage (ADLS container) and created Delta tables at destination but it it fails to ingest re
Learn to perform a SAML trace and generate SAML Trace JSON file using browser plug-ins. Streamsets support team uses the file to trace the SAML assertions occurring between your identity provider and Control-Hub to troubleshoot your sign-in issues. Use the links below to download and install the SAML tracer plug-in for your browser: Note: The links and steps provided here were correct at time of publishing. Mozilla Firefox: https://addons.mozilla.org/en-US/firefox/addon/saml-tracer/ Google Chrome: https://chrome.google.com/webstore/detail/saml-tracer/mpdajninpobndbfcldcmbpnnbhibjmch When the plug-in is added, Click the newly added SAML tracer icon on the upper-right add-in menu for your respective browser. This opens the SAML Tracer dialog box. The SAML tracer dialog box records and displays details as shown below. Note the occasional SAML tags shown at the right indicating SAML assertions being passed. Re-produce the issue. use the SAML Tracer dialog to navigate to Export tab
I have a pipeline migrating oracle db to salesforce which gets a couple of product attributes from salesforce for all previously migrated products in salesforce. Each record in the pipeline is processing one product and needs to reference this map, which uses local caching to only call salesforce once to build the product map of everything (rather than call for every product being processed - which is tioo slow and uses too many API calls).With this approach, every record gets the map of all products. The approach works with small batch sizes of 50, but runs out of heap memory with batch sizes bigger than that. Is there a way for every record to access this large map of Prouct information loaded from salesforce without duplicating it inside of every record?
Kubernetes Deployment fails with the below errorAck Error for Event '64627d8f-ccc8-494e-bbd3-e9750f361a68:dpmsupport'. Reason : java.lang.IllegalStateException: ConfigMap is not a supported kind in the deployment specControl-agent (Provisioning agent ) cannot create config-maps or secrets. The only resources control agent can create are deployments, services, ingress and hpa.If you want to use these resources then you need to create this separately and call it in deployment manifest.
I need to extract data from an API using OAuth2 connection. As per the data, they provide /cursor at the end of each page and that cursor can be used to get the records from next page.In Pagination tab, I used Link with Response field and tried to add Stop Condition with /cursor. but, not able to handle this scenario.Can someone please help. Thanks in Advance!!
Comparing with relational databases, at times we want to have some id field in our dataset which is always increasing(usually for every record insertion). In general, this is useful for indexing, ordering, to distinguish each record or in simple words to maintain integrity of the database. This might not be the case with distributed systems. For Spark, data is mostly written to destination in partitions(that’s how we achieve parallelism). Since these partitions are written in parallel, we can not have consecutive id generation capability which is monotonically increasing. Spark has a function monotonically_increasing_id() which is helpful to generate increasing id at the partition level.Below is what Spark documentation has to say about it.A column that generates monotonically increasing 64-bit integers.The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive. The current implementation puts the partition ID in the upper 31 bits, and the record numb
As we end our next quarter as a community, it is time to highlight our next community champion! Our next Community champion is Bikram Rout! @Bikram joined our community early last year and today is one of the most helpful members within our slack and forum. If you had a question within the forum, you probably have received help from Bikram. So let's give a hand to Bikram and congratulate our next StreamSets Community Champion. This year at DataOps Summit we are having a Hackathon (Free to Join). If you are interested in the event and or the hackathon check the details here. If you are joining us at DataOps Summit make sure to use our Community code for 30% off the event registration! “Comm30” What's New DataOps Summit registration is open! Register Today. Use “Comm30” at check out for 30% off event registration. Webinar - Native Transformations for Snowflake Data Cloud General availability is here! StreamSets Transformer for Snowflake. Read here Industry News / Helpful Reads D
We are excited to announce our new offering: StreamSets Transformer for Snowflake! Learn details about the new offering and about how we're shifting to a higher gear. Gain the insights here, https://blog.softwareag.com/streamsets-snowflake
Conference season begins–connecting with your fellow data lovers is near! Who will be attending Snowflake Summit or DataOp Summit?DataOps Summit is an IN-PERSON 2-day event and THE premier gathering place for data technology leaders and professionals to gain perspective on the present and future of DataOps. Plus, join us for a Hackathon after training sessions on Aug 29th, Day 0. Stay tuned for more information!What's NewDataOps Summit registration is open! Register Today. Webinar on Wednesday, June 22 @ 10AM PT - Native Transformations for Snowflake Data Cloud Engineers - register for the webinar here. Join us at Snowflake Summit. Schedule a demo here.Industry News / Helpful ReadsTransformer for Snowflake. Learn more about and get access to our newest engine. Join us at Snowflake Summit. Schedule a demo here. Ebook: Data Engineers' Handbook for Snowflake Stories about Data Engineering on Medium Kafka Streaming: Live Streaming Kafka Application to Cassandra - Blog Data Pipeline Archit
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