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Apache Kudu

57
198
+ 1
9
Apache Flink

400
625
+ 1
35
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Apache Flink vs Apache Kudu: What are the differences?

Developers describe Apache Flink as "Fast and reliable large-scale data processing engine". Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala. On the other hand, Apache Kudu is detailed as "Fast Analytics on Fast Data. A columnar storage manager developed for the Hadoop platform". A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

Apache Flink and Apache Kudu can be primarily classified as "Big Data" tools.

"Unified batch and stream processing" is the primary reason why developers consider Apache Flink over the competitors, whereas "Realtime Analytics" was stated as the key factor in picking Apache Kudu.

Apache Flink and Apache Kudu are both open source tools. Apache Flink with 10K GitHub stars and 5.37K forks on GitHub appears to be more popular than Apache Kudu with 801 GitHub stars and 268 GitHub forks.

According to the StackShare community, Apache Flink has a broader approval, being mentioned in 29 company stacks & 97 developers stacks; compared to Apache Kudu, which is listed in 5 company stacks and 21 developer stacks.

Advice on Apache Kudu and Apache Flink
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 227.3K views

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Replies (2)
Recommends
Elasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 129.2K views
Recommends
Apache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

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Pros of Apache Kudu
Pros of Apache Flink
  • 9
    Realtime Analytics
  • 15
    Unified batch and stream processing
  • 8
    Easy to use streaming apis
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 3
    Open Source
  • 1
    Low latency

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Cons of Apache Kudu
Cons of Apache Flink
  • 0
    Restart time
    Be the first to leave a con

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    What is Apache Kudu?

    A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

    What is Apache Flink?

    Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Apache Kudu?
    What companies use Apache Flink?
    See which teams inside your own company are using Apache Kudu or Apache Flink.
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    What tools integrate with Apache Kudu?
    What tools integrate with Apache Flink?

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    What are some alternatives to Apache Kudu and Apache Flink?
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    HBase
    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    Apache Impala
    Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    See all alternatives