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GoogleCloudPlatform
DataflowJavaSDK
Data & Databases2016–2016
GoogleCloudPlatform / DataflowJavaSDK · Archived

DataflowJavaSDK

Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines.

3Distinct people
3Mentions
0Explicit recommenders
848GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 6:01:08 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month3 dated mentions

Observed mentions: 2016-01-21 21:16:19 UTC to 2016-10-17 14:39:13 UTC.

012016-01-01 00:00:00 UTC to 2016-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJan 20162016-02-01 00:00:00 UTC to 2016-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-03-01 00:00:00 UTC to 2016-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2016-04-01 00:00:00 UTC to 2016-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-05-01 00:00:00 UTC to 2016-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMay 20162016-06-01 00:00:00 UTC to 2016-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-07-01 00:00:00 UTC to 2016-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-08-01 00:00:00 UTC to 2016-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-09-01 00:00:00 UTC to 2016-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-10-01 00:00:00 UTC to 2016-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsOct 2016

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jan 20162016-01-01 00:00:00 UTC2016-02-01 00:00:00 UTC101
Feb 20162016-02-01 00:00:00 UTC2016-03-01 00:00:00 UTC000
Mar 20162016-03-01 00:00:00 UTC2016-04-01 00:00:00 UTC101
Apr 20162016-04-01 00:00:00 UTC2016-05-01 00:00:00 UTC000
May 20162016-05-01 00:00:00 UTC2016-06-01 00:00:00 UTC000
Jun 20162016-06-01 00:00:00 UTC2016-07-01 00:00:00 UTC000
Jul 20162016-07-01 00:00:00 UTC2016-08-01 00:00:00 UTC000
Aug 20162016-08-01 00:00:00 UTC2016-09-01 00:00:00 UTC000
Sep 20162016-09-01 00:00:00 UTC2016-10-01 00:00:00 UTC000
Oct 20162016-10-01 00:00:00 UTC2016-11-01 00:00:00 UTC101

What Hacker News says

1 sources
I'm skeptical that any other language will be nearly as easy write, operate, and maintain in production as Java. It's really easy to administrate Java applications. You can connect to the JVM and examine what objects are in the heap, or request a thread dump and see what threads the application has launched and what they're doing. There are rarely any (JVM-level) correctness issues since code is safe and has a simple exception model - JVMs are rock solid and virtually never crash. There are advanced profiling tools available, and simple ones built in. It's easy to deploy applications due to Java's jar and class model. You can inspect a class and see its code as-deployed. Building Java code is simple, so there are rarely any build tooling or reproducibility issues. Java also has excellent tools for logging and metrics emission, like Log4j
Read the rest
/ Slf4j and utilities like Metrics. These are just a few of the system-level features that are critical in large-scale deployments, and in practice these elements matter quite a lot for managing production apps. The language itself is only part of the story. The Java language has also been improving substantially. Java 1.8 launched a lot of great features, like lambdas, functional interfaces, streams, and other modern elements that give me most of what I expect from a modern language and allow me to write clear and concise code that is statically typed, in the context of really strong IDEs. Java's portability makes it straightforward to develop an application from any platform (not just the server platform you deploy on). IDE support is crucial and frequently overlooked. Automatic code completion, refactoring, and inspection ("show all usages of this method") are essential to working on large code bases together as a team. Java also has style enforcement and static analysis tools like FindBugs, Fortify, CheckStyle, etc. Java's open source community is extremely strong - look at all of the work happening under the Apache umbrella such as Hadoop, Spark, and Hive [1] or Google's umbrella, like Google Guava [2] or the Dataflow SDK [3]. There are excellent concurrency tools like java.util.concurrent, Guava's futures, and Akka. I agree with meddlepal on this one. It's hard to do better than Java for the backend. [1] https://projects-old.apache.org/indexes/language.html#Java [2] https://code.google.com/p/guava-libraries/ [3] https://github.com/GoogleCloudPlatform/DataflowJavaSDK
In Why we choose Java instead of a polyglot stack

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