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Apache Spark

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Apache Spark vs Sqoop: What are the differences?

Apache Spark: 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; Sqoop: A tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores. It is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases of The Apache Software Foundation.

Apache Spark can be classified as a tool in the "Big Data Tools" category, while Sqoop is grouped under "Database Tools".

Apache Spark is an open source tool with 22.9K GitHub stars and 19.7K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.

Uber Technologies, Slack, and Shopify are some of the popular companies that use Apache Spark, whereas Sqoop is used by Auto Trader, Adaptly, and Kobalt Music. Apache Spark has a broader approval, being mentioned in 356 company stacks & 564 developers stacks; compared to Sqoop, which is listed in 9 company stacks and 6 developer stacks.

Advice on Apache Spark and Sqoop
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 364.1K 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
ElasticsearchElasticsearch

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 · 236.1K views
Recommends
Apache SparkApache 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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Manoj Das
Senior Research Analyst at Mu Sigma · | 1 upvotes · 1.9K views
Needs advice
on
Apache SparkApache Spark
and
SqoopSqoop

Will my data migration from a relational database be as fast as using Sqoop in spark by means of JDBC connection. What are the recommended spark config setting I need to ensure to see I have equal or more performance coming through Spark?. Would spark be limited in any way if I did it all instead of sqoop

The most important factors for me are performance and hopefully want to stick to spark, while I have everything else I do from here.

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Replies (1)
Recommends
Apache SparkApache Spark

Scoop requires alot of configuration tweaks e.g. link creations etc etc. I'm recommending spark over scoop because it has alot of things embedded in place - e.g. data chunking basing on certain keys etc - ofcourse it also depends on the implementation - spark supports various stacks of data protocols and APIs e.g. spark-solr, mongo-spark, spark-elasticsearch etc etc. the advises for configuration settings would be more generic rather than something concrete - because it really depends on the database or the data source/target you want to use but i would generally recommend running spark with YARN and use a corresponding number of executors - depending on your cluster configuration ram/cpu/drives etc - you can read more on the settings in the official spark docs for sure. --- Cheers

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Pros of Apache Spark
Pros of Sqoop
  • 59
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 7
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    In memory Computation
  • 2
    Machine learning libratimery, Streaming in real
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    Cons of Apache Spark
    Cons of Sqoop
    • 3
      Speed
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      - No public GitHub repository available -

      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.

      What is Sqoop?

      It is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases of The Apache Software Foundation

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

      Jobs that mention Apache Spark and Sqoop as a desired skillset
      CBRE
      Netherlands Noord-Holland Amsterdam
      CBRE
      Netherlands Noord-Brabant 's-Hertogenbosch
      CBRE
      Netherlands Noord-Holland Amsterdam
      CBRE
      Netherlands Noord-Holland Amsterdam
      CBRE
      United Kingdom of Great Britain and Northern Ireland England London
      CBRE
      Netherlands Groningen Eemshaven
      What companies use Apache Spark?
      What companies use Sqoop?
      See which teams inside your own company are using Apache Spark or Sqoop.
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      What tools integrate with Apache Spark?
      What tools integrate with Sqoop?
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        Blog Posts

        Mar 24 2021 at 12:57PM

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        What are some alternatives to Apache Spark and Sqoop?
        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.
        Splunk
        It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
        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.
        Apache Beam
        It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
        Apache Flume
        It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
        See all alternatives