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This repository has been archived by the owner on Feb 12, 2022. It is now read-only.

Releases: salesforce/pyplyn

Release 10

06 Oct 16:05
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Introduction

Pyplyn (meaning pipeline in Afrikaans) is an open source tool that
extracts data from various sources transforms and gives it meaning, and sends it to other systems for consumption.

How does Pyplyn work

Release notes

  • Faster processing speed with the use of RxJava (4.3x faster, tested on our reference dataset)
  • Cleaner code, mainly after converting models Immutables-annotated abstract classes
  • Support mutual TLS authentication for endpoints, by specifying a Java keystore and password
  • Connect, read, and write timeouts can now be defined for each connector
  • All Jackson-based models can now be serialized (with the type specifier field)
  • AppConfig.Global.minRepeatIntervalMillis was deprecated (replaced with AppConfig.Global.runOnce)
  • Added a bash script for managing the service's lifecycle (start, stop, restart, logs, etc.)
  • Since 10.0.0, Pyplyn releases follow Semantic versioning guidelines.

Read more about Pyplyn, on the project's documentation page.

Release 9.0

28 Apr 15:51
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Introduction

Pyplyn is a scalable time-series data collector with ETL capabilities that allows you to ship metrics and visualize system health in real-time.

Release notes

  • Added a feature in the HighestValue transform, to support tagging the submitted Refocus samples with the original datapoint's timestamp, by specifying the tagMessageBody option
{
  "name" : "HighestValue",
  "tagMessageBody" : "ORIGINAL_TIMESTAMP"
}
  • Updated Argus client to be compatible with Release 2.6.0
  • Fixed a bug in the Argus client that was causing null fields to be serialized, causing the endpoint to return 400/Bad Request when passing Notification.severityLevel=null
  • Fixed a bug in Refocus client to prevent read-only fields to be serialized
  • Refactor modular system; allow extension projects to replace modules
  • Faster startup; trigger main execution just after configurations are made available
  • Separated deserialization functionality into own method; better test initialization
  • Improved remote client logging

Release 8.0

10 Apr 13:01
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Introduction

Pyplyn is a scalable time-series data collector with ETL capabilities that allows you to ship metrics and visualize system health in real-time.

Release notes

Release 7.0

06 Apr 14:30
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Introduction

Pyplyn is a scalable time-series data collector with ETL capabilities that allows you to ship metrics and visualize system health in real-time.

Release notes

  • ability to retrieve multiple Refocus samples using wildcards
  • added a new transform: ThresholdMetForDuration
  • fixed an issue that was causing the configuration update process to fail
  • improved unit-tests and code coverage
  • simplified ETL/Guice module definitions
  • various bug fixes

Pyplyn 5.0

28 Feb 18:21
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Introduction

Pyplyn is a scalable time-series data collector with ETL capabilities that allows you to ship metrics and visualize system health in real-time.

Release notes

  • Fixed Argus and Refocus extract processor cache bugs; caching now correctly works
  • Fixed resource leak when reading configuration files
  • Fixed thread leak bug with OkHttp connection pools and WeakReference-based caching
  • Moved health logic to count events instead of using rate/sec
  • Improved logging
  • Ability to filter alerts by owner name in the argus client
  • Updated contributor guide
  • Added more unit-tests
  • Configured Travis-CI
  • Added new project logo
  • Updated Hazelcast and Retrofit dependencies
  • Findbugs check and fixes
  • Cleaned up pom.xml

Pyplyn 4.0

27 Jan 17:23
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Quick start

  1. Clone this repository
  2. Build the project with mvn package
  3. Run pyplyn with java -jar target/pyplyn-4.0.jar --config /path/to/app-config.json
  4. Read the API reference for an in-depth introduction and quick start guide

Also, check out the README.md file and our open-source contributor guide.

For any questions, open a new issue and we'll respond as soon as possible.

Thank you!