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Apache Ignite

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Apache Ignite vs Apache Spark: What are the differences?

Developers describe Apache Ignite as "An open-source distributed database, caching and processing platform *". It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale. On the other hand, *Apache Spark** is detailed as "Fast and general engine for large-scale data processing". 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 Ignite belongs to "In-Memory Databases" category of the tech stack, while Apache Spark can be primarily classified under "Big Data Tools".

Some of the features offered by Apache Ignite are:

  • Memory-Centric Storage
  • Distributed SQL
  • Distributed Key-Value

On the other hand, Apache Spark provides the following key features:

  • Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
  • Write applications quickly in Java, Scala or Python
  • Combine SQL, streaming, and complex analytics

Apache Ignite and Apache Spark are both open source tools. It seems that Apache Spark with 22.9K GitHub stars and 19.7K forks on GitHub has more adoption than Apache Ignite with 2.67K GitHub stars and 1.3K GitHub forks.

According to the StackShare community, Apache Spark has a broader approval, being mentioned in 356 company stacks & 564 developers stacks; compared to Apache Ignite, which is listed in 4 company stacks and 4 developer stacks.

Advice on Apache Ignite and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 181K 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 · 89K 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 Ignite
Pros of Apache Spark
  • 3
    Written in java. runs on jvm
  • 3
    Free
  • 2
    Load balancing
  • 2
    High Avaliability
  • 2
    Rest interface
  • 2
    Sql query support in cluster wide
  • 1
    Multiple client language support
  • 1
    Better Documentation
  • 1
    Distributed compute
  • 1
    Distributed Locking
  • 1
    Easy to use
  • 58
    Open-source
  • 48
    Fast and Flexible
  • 7
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 6
    Great for distributed SQL like applications
  • 3
    Works well for most Datascience usecases
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
  • 0
    Interactive Query

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Cons of Apache Ignite
Cons of Apache Spark
    Be the first to leave a con
    • 3
      Speed

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    What is Apache Ignite?

    It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

    What is 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.

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

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    What companies use Apache Spark?
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    What are some alternatives to Apache Ignite and Apache Spark?
    Redis
    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    Hazelcast
    With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    See all alternatives
    How developers use Apache Ignite and Apache Spark
    Wei Chen uses
    Apache Spark

    Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.

    Ralic Lo uses
    Apache Spark

    Used Spark Dataframe API on Spark-R for big data analysis.

    Kalibrr uses
    Apache Spark

    We use Apache Spark in computing our recommendations.

    Dotmetrics uses
    Apache Spark

    Big data analytics and nightly transformation jobs.

    brenoinojosa uses
    Apache Spark

    Data retrieval and analysis of Cassandra.