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Apache Impala

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Apache Spark

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Apache Spark vs Apache Impala: What are the differences?

What is 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.

What is Apache Impala? Real-time Query for Hadoop. 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.

Apache Spark and Apache Impala belong to "Big Data Tools" category of the tech stack.

Some of the features offered by Apache Spark are:

  • 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

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

  • Do BI-style Queries on Hadoop
  • Unify Your Infrastructure
  • Implement Quickly

"Open-source" is the top reason why over 45 developers like Apache Spark, while over 7 developers mention "Super fast" as the leading cause for choosing Apache Impala.

Apache Spark and Apache Impala 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 Impala with 2.19K GitHub stars and 825 GitHub forks.

According to the StackShare community, Apache Spark has a broader approval, being mentioned in 356 company stacks & 566 developers stacks; compared to Apache Impala, which is listed in 17 company stacks and 37 developer stacks.

Advice on Apache Impala and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 361.6K 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 · 234K 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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Pros of Apache Impala
Pros of Apache Spark
  • 11
    Super fast
  • 1
    Load Balancing
  • 1
    Replication
  • 1
    Scalability
  • 1
    Distributed
  • 1
    High Performance
  • 1
    Massively Parallel Processing
  • 1
    Open Sourse
  • 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 Impala
Cons of Apache Spark
    Be the first to leave a con
    • 3
      Speed

    Sign up to add or upvote consMake informed product decisions

    What is 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.

    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.

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    Jobs that mention Apache Impala and Apache Spark as a desired skillset
    CBRE
    United Kingdom of Great Britain and Northern Ireland England Feltham
    CBRE
    United States of America Texas Richardson
    CBRE
    Philippines National Capital Region Makati City
    CBRE
    United States of America Texas Richardson
    What companies use Apache Impala?
    What companies use Apache Spark?
    See which teams inside your own company are using Apache Impala or Apache Spark.
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    What tools integrate with Apache Impala?
    What tools integrate with Apache Spark?

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    Blog Posts

    Mar 24 2021 at 12:57PM

    Pinterest

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    MySQLKafkaApache Spark+6
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    Aug 28 2019 at 3:10AM

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    What are some alternatives to Apache Impala and Apache Spark?
    Presto
    Distributed SQL Query Engine for Big Data
    Apache Drill
    Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.
    Apache Hive
    Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
    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.
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    See all alternatives