Deep Java Library (DJL) VS Apache Flink

Compare Deep Java Library (DJL) vs Apache Flink and see what are their differences.

Our great sponsors
  • Scout APM - Less time debugging, more time building
  • SonarLint - Clean code begins in your IDE with SonarLint
  • SaaSHub - Software Alternatives and Reviews
Deep Java Library (DJL) Apache Flink
7 3
2,617 19,221
3.1% 1.3%
9.4 10.0
3 days ago 3 days ago
Java Java
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Deep Java Library (DJL)

Posts with mentions or reviews of Deep Java Library (DJL). We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-03.

Apache Flink

Posts with mentions or reviews of Apache Flink. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-02.
  • DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
    21 projects | | 2 Jun 2022
    Apache Drill, Druid, Flink, Hive, Kafka, Spark
  • Computation reuse via fusion in Amazon Athena
    2 projects | | 20 May 2022
    It took me some time to get a good grasp of the power of SQL; and it really kicked in when I learned about optimization rules. It's a program that you rewrite, just like an optimizing compiler would.

    You state what you want; you have different ways to fetch and match and massage data; and you can search through this space to produce a physical plan. Hopefully you used knowledge to weight parts to be optimized (table statistics, like Java's JIT would detect hot spots).

    I find it fascinating to peer through database code to see what is going on. Lately, there's been new advances towards streaming databases, which bring a whole new design space. For example, now you have latency of individual new rows to optimize for, as opposed to batch it whole to optimize the latency of a dataset. Batch scanning will be benefit from better use of your CPU caches.

    And maybe you could have a hybrid system which reads history from a log and aggregates in a batched manner, and then switches to another execution plan when it reaches the end of the log.

    If you want to have a peek at that here are Flink's set of rules [1], generic and stream-specific ones. The names can be cryptic, but usually give a good sense of what is going on. For example: PushFilterIntoTableSourceScanRule makes the WHERE clause apply the earliest possible, to save some CPU/network bandwidth further down. PushPartitionIntoTableSourceScanRule tries to make a fan-out/shuffle happen the earliest possible, so that parallelism can be made use of.


  • Avro SpecificRecord File Sink using apache flink is not compiling due to error incompatible types: FileSink<?> cannot be converted to SinkFunction<?>
    3 projects | | 14 Sep 2021
    [1]: [2]: [3]: [4]: [5]:

What are some alternatives?

When comparing Deep Java Library (DJL) and Apache Flink you can also consider the following projects:

Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.

mediapipe - Cross-platform, customizable ML solutions for live and streaming media.

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

H2O - Sparkling Water provides H2O functionality inside Spark cluster

Apache Kafka - Mirror of Apache Kafka

Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing

Gearpump - Lightweight real-time big data streaming engine over Akka

Tribuo - Tribuo - A Java machine learning library

Scio - A Scala API for Apache Beam and Google Cloud Dataflow.

Jupyter Scala - A Scala kernel for Jupyter