Spring Team
Mark Pollack

Mark Pollack

Spring XD co-lead/Spring Data Lead

New York, NY

Mark Pollack is a software engineer with Pivotal and is the co-lead of the Spring Cloud Data Flow and Spring XD projects He has been a contributor to many Spring projects dating back to the Spring Framework in 2003 as well as founding the Spring.NET and Spring Data projects.
Blog Posts by Mark Pollack

Spring Cloud Data Flow 1.2 GA released

On behalf of the team, I am pleased to announce the general availability of Spring Cloud Data Flow 1.2 across a range of platforms

Here are the relevant links to documentation and getting started guides.

Highlights of the 1.2 release:

Composed Tasks

This release introduces Composed Tasks ! This feature provides the ability to orchestrate a flow of tasks as a cohesive unit-of-work. A complex ETL pipeline may include executions in sequence, parallel, conditional transitions, or a combination of all of the above. The composed task feature comes with DSL primitives and an interactive graphical interface to quickly build these type of topologies more easily. You can read more about it from the reference guide.

An ETL job, for example, may include multiple steps. Each step in the topology can be built as a finite short-lived Spring Cloud Task application. The orchestration of multiple tasks as steps can be easily defined with the help of the Data Flow Task DSL.

task create simple-etl --definition "extractDbToHDFS &&
      <analysisInSpark || enrichAndLoadHawq> &&
      <populateMgmtDashboard || runRegulatoryReport || loadAnalyticsStore>"

This will first run extractDbToHDFS and then run analysisInSpark and enrichAndLoadHawq in parallel, waiting for the both of them to complete before running the three remaining tasks in parallel and waiting for them to all complete before ending the job. The graphical representation of this topology looks like the following.

Visualization of Composed Tasks

Real-time Metrics and Monitoring

Real-time metrics are now part of the operational view of deployed streams. The applications that are part of a stream publish metrics contained in their Spring Boot /metrics actuator endpoint. This includes send and receive messages rates. A new server, the Spring Cloud Data Flow Metrics Collector, collects these metrics and calculates aggregate message rates. The Data Flow server queries the Metrics Collector to support showing message rates in the UI and in the shell. For more details about the architecture, refer to the Monitoring Deployed Applications Section in the reference guide.

The screenshot below shows the aggregate message rates for a time | log stream with three instances of the time and log applications. Each dot below the main application box shows the message rates for each individual application along with a guid value that can be used to identify the application on the platform where they are running.

Visualization of Input and Output Rates in Flo

The Runtime tab, shown below, also had improvements to show message rates and any other metrics exposed by the platform. For the script savvy users, the shell experience also includes these details via the runtime apps command.

Runtime Apps UI

Companion Artifact

The companion artifact support introduced in 1.2 M3 has had some improvements. The bulk registration workflow now eagerly resolves and downloads the metadata artifacts for all the out-of-the-box applications. This comes handy in the Shell or UI when reviewing the supported properties for each application.

OAUTH Improvements

This change will provide an additional option for REST-API users. Instead of providing a username:password combination via BasicAuth, users will now have the ability to retrieve an OAuth2 Access token from their OAuth2 provider directly and then provide the Access Token in the HTTP header, when invoking RESTful calls against a secured Spring Cloud Data Flow setup.

Role based access

Add role-based access control to define who has access to create, deploy, destroy, or view streams/tasks. This works seamlessly in coordination with the supported authentication methods.

Bug reporting

A new REST endpoint and About page in the Dashboard to collect server implementation details to the clipboard for use in bug reporting.

Spring Cloud Stream App Starters - Bacon.RELEASE

The Stream App Starters Bacon.RELEASE is now generally available which provides you a range of sources, processors, and sinks to get started creating stream. All the out-of-the-box stream applications build upon Spring Cloud Stream Chelsea.RELEASE and Spring Cloud Dalston.RELEASE foundation. There were several enhancements and bug-fixes to the existing applications and this release-train also brings new applications such as MongoDB-sink, Aggregator-processor, Header-Enricher-processor, and PGCopy-sink.

For convenience, we have generated the bit.ly links that includes the latest coordinates for docker and maven artifacts.

Spring Cloud Task App Starters - Belmont.RELEASE

The Task App Starters Belmont.RELEASE release is now complete. To support Composed Task feature in Spring Cloud Data Flow, we have added a new out-of-the-box application named Composed Task Runner. This is a task that executes others tasks in a directed graph as specified by a DSL that is passed in via the --graph command line argument.

The Belmont.RELEASE builds upon Spring Cloud Task 1.2 RELEASE and Spring Cloud Dalston.RELEASE foundation.

For convenience, we have generated the bit.ly links that includes the latest coordinates for docker and maven artifacts.

What’s Next?

An immediate goal is adding more automated integration tests and to expose this as an additional user facing feature. You can track that work here.

Beyond the 1.2.x line, we are going to start planning for the 2.0 version. Some general themes are support for deploying individual applications and keeping track of application deployment properties and metadata such as the application version. This functionality would build up into supporting a rich Continuous Delivery theme at the application level that also extends to "editing" streams at runtime. In addition, we are also looking into supporting functions, either "in-line" as Java code or compiled java.util.Function s to be a first class programming model for data processing a stream.

Feedback is important. Please reach out to us in StackOverflow and GitHub for questions and feature requests. We also welcome contributions! Any help improving the Spring Cloud Data Flow ecosystem is appreciated.

Read more...

Spring Cloud Data Flow 1.2 RC1 released

On behalf of the team, I am pleased to announce the first release candidate of Spring Cloud Data Flow 1.2.

Note: A great way to start using this new release is to follow the Getting Started Guide in the reference documentation.

Highlights of the 1.2 RC1 release:

Composed Tasks

This release introduces Composed Tasks ! This feature provides the ability to orchestrate a flow of tasks as a cohesive unit-of-work. A complex ETL pipeline may include executions in sequence, parallel, conditional transitions, or a combination of all of the above. The composed task feature comes with DSL primitives and an interactive graphical interface to quickly build these type of topologies more easily. You can read more about it from the reference guide.

An ETL job, for example, may include multiple steps. Each step in the topology can be built as a finite short-lived Spring Cloud Task application. The orchestration of multiple tasks as steps can be easily defined with the help of the Data Flow Task DSL.

task create simple-etl --definition "extractDbToHDFS &&
      <analysisInSpark || enrichAndLoadHawq> &&
      <populateMgmtDashboard || runRegulatoryReport || loadAnalyticsStore>"

This will first run extractDbToHDFS and then run analysisInSpark and enrichAndLoadHawq in parallel, waiting for the both of them to complete before running the three remaining tasks in parallel and waiting for them to all complete before ending the job. The graphical representation of this topology looks like the following.

Visualization of Composed Tasks

Real-time Metrics and Monitoring

Real-time metrics are now part of the operational view of deployed streams. The applications that are part of a stream publish metrics contained in their Spring Boot /metrics actuator endpoint. This includes send and receive messages rates. A new server, the Spring Cloud Data Flow Metrics Collector, collects these metrics and calculates aggregate message rates. The Data Flow server queries the Metrics Collector to support showing message rates in the UI and in the shell. For more details about the architecture, refer to the Monitoring Deployed Applications Section in the reference guide.

The screenshot below shows the aggregate message rates for a time | log stream with three instances of the time and log applications. Each dot below the main application box shows the message rates for each individual application along with a guid value that can be used to identify the application on the platform where they are running.

Visualization of Input and Output Rates in Flo

The Runtime tab, shown below, also had improvements to show message rates and any other metrics exposed by the platform. For the script savvy users, the shell experience also includes these details via the runtime apps command.

Runtime Apps UI

Companion Artifact

The companion artifact support introduced in 1.2 M3 has had some improvements. The bulk registration workflow now eagerly resolves and downloads the metadata artifacts for all the out-of-the-box applications. This comes handy in the Shell or UI when reviewing the supported properties for each application.

OAUTH Improvements

This change will provide an additional option for REST-API users. Instead of providing a username:password combination via BasicAuth, users will now have the ability to retrieve an OAuth2 Access token from their OAuth2 provider directly and then provide the Access Token in the HTTP header, when invoking RESTful calls against secured Spring Cloud Data Flow setup.

Spring Cloud Stream App Starters - Bacon.RELEASE

Bacon.RELEASE is now generally available. All the out-of-the-box stream applications build upon Spring Cloud Stream Chelsea.RELEASE and Spring Cloud Dalston.RELEASE foundation. There were several enhancements and bug-fixes to the existing applications and this release-train also brings new applications such as MongoDB-sink, Aggregator-processor, Header-Enricher-processor, and PGCopy-sink.

For convenience, we have generated the bit.ly links that includes the latest coordinates for docker and maven artifacts.

Spring Cloud Task App Starters - Belmont.RC1

The App Starters Belmont.RC1 release is now complete. To support Composed Task feature in Spring Cloud Data Flow, we have added a new out-of-the-box application named Composed Task Runner. This is a task that executes others tasks in a directed graph as specified by a DSL that is passed in via the --graph command line argument.

The Belmont.RC1 builds upon Spring Cloud Task 1.2 RC1 and Spring Cloud Dalston.RELEASE foundation.

For convenience, we have generated the bit.ly links that includes the latest coordinates for docker and maven artifacts.

What’s Next?

The 1.2.0.RELEASE is around the corner. We are aiming to wrap it over the next 2-3 weeks. Spring Cloud Data Flow’s runtime implementations will catch up and adapt to this foundation momentarily after the core release.

Feedback is important. Please reach out to us in StackOverflow and GitHub for questions and feature requests. We also welcome contributions! Any help improving the Spring Cloud Data Flow ecosystem is appreciated.

Read more...

Spring Cloud Data Flow 1.1 GA released

On behalf of the team, I am pleased to announce the GA release of Spring Cloud Data Flow 1.1. Follow the links in the getting started guide to download the local server implementation and shell to create Stream and Tasks.

General highlights of the 1.1 GA Release include:

Read more...

Spring Cloud Data Flow 1.1 RC1 Released

On behalf of the team, I am pleased to announce the first release candidate of Spring Cloud Data Flow 1.1. Follow the links in the getting started guide to download the local server implementation and shell to create Stream and Tasks.

The 1.1 RC1 release includes the following new features and improvements:

  • Builds upon Camden.SR2 release improvements

  • LDAP authentication is now supported with SSL

  • Portable deployment properties for memory and cpu are in place for support across various runtime implementations

  • Passing Java Options to the local JVM when launching application is now supported

  • UI Improvements

    • List pages now support sorting

    • Server-side search support for stream and task list pages

    • Content-assist for bulk task definitions including the support for incremental validations of task application properties

  • Add content assist support for tasks in the shell

  • Thanks to the community for adding DB2 support for the TaskRepository

  • Documentation on how to use Spring Boot Admin to visualize server metrics

Read more...

Spring Cloud Data Flow 1.1 M2 Released

On behalf of the team, I am pleased to announce the release of the second milestone of Spring Cloud Data Flow 1.1. You can download the local server that is part of this release here.

The 1.1 M2 release includes the following new features and improvements:

  • Builds upon Boot 1.4.1 and Spring Cloud Camden improvements

  • Task application properties can now be referenced using non-prefixed property names

  • Add visual representation for related streams. This representation also includes nested TAPs and the downstream processing nodes in an overall topology view.

Read more...

Spring Cloud Data Flow 1.0 GA released

On behalf of the team, I’m excited to announce the 1.0 GA release of Spring Cloud Data Flow!

Note
A great way to start using this new release is to follow the Getting Started section of the reference documentation. It uses a Data Flow server that runs on your computer and deploys a new process for each application.

Spring Cloud Data Flow (SCDF) is an orchestration service for data microservices on modern runtimes. SCDF lets you describe data pipelines that can either be composed of long lived streaming applications or short lived task applications and then deploys these to platform runtimes that you may already be using today, such as Cloud Foundry, Apache YARN, Apache Mesos, and Kubernetes. We provide a wide range of stream and task applications so you can get started right away to develop solutions for use-cases such as data ingestion, real-time analytics and data import/export.

Read more...

Spring Cloud Data Flow 1.0 RC1 released

On behalf of the team I am pleased to announce the release of Spring Cloud Data Flow 1.0 RC1.

Several exciting new features have been added in this release which carry over to the other Data Flow Server implementations that were also released today.

Follow the links above for details on features unique to each individual runtime platform. The highlights of the release are:

Read more...

Spring XD 1.3.1 GA and Flo for Spring XD 1.0.1 GA released

Today we are pleased to announce the general availability of Spring XD 1.3.1 and Flo for Spring XD 1.0.1

  • Spring XD 1.3.1 GA: zip, brew and rpm.
  • Flo for Spring XD 1.0.1 GA: zip.

Here are some highlights of bug fixes and general improvements. Consult the JIRA release notes for the full list of issues fixed.

Read more...

Spring Cloud Stream 1.0 M3 and Data Flow 1.0 M2 released

On behalf of the team, I am pleased to announce several releases in the Spring Cloud Stream and Spring Cloud Data Flow family of projects.

Spring Cloud Stream 1.0 M3 introduces the following features

Spring Cloud Stream Modules 1.0 M2 adds many new modules with updated documentation.

  • Sources: File, Load Generator, sftp, and tcp
  • Processors: httpclient, PMML, and Splitter
  • Sinks: Cassandra, Field Value Counter, file, ftp, gemfire, HDFS Dataset, JDBC, tcp, throughput, and websocket
Read more...

Spring XD 1.3 GA and Flo for Spring XD 1.0 GA released

Today we are pleased to announce the general availability of Spring XD 1.3 and Flo for Spring XD 1.0.

  • Spring XD 1.3 GA: zip, brew and rpm.
  • Flo for Spring XD 1.0 GA: zip.

In addition to bug fixes we have also added several new features in the 1.3 release line

  • Job Composition DSL allows for the creation of a complex graph of job executions.
  • Flo for Spring XD designer supports creating composed jobs.
  • Admin UI supports execution history of composed jobs.
  • Cassandra Sink and Header Enricher Processor
  • Gpfdist sink now supports update operations and full range of control file options
  • Spark 1.3.1 Support
  • A timeout value for flushing writes to HDFS in order to ensure data is persisted on the HDFS DataNode’s disks.
  • General dependency upgrades, Spring Data Gosling, SI 4.2, and Boot 1.2
  • Hadoop distribution version updates to Apache Hadoop 2.7.1 and Hortonworks Data Platform 2.2. Pivotal Hadoop 2.1, 3.0 and Cloudera Hadoop 5.3
Read more...