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Apache Storm

185
277
+ 1
24
Mercurial

218
208
+ 1
105
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Apache Storm vs Mercurial: What are the differences?

Developers describe Apache Storm as "Distributed and fault-tolerant realtime computation". Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate. On the other hand, Mercurial is detailed as "A free, distributed source control management tool". Mercurial is dedicated to speed and efficiency with a sane user interface. It is written in Python. Mercurial's implementation and data structures are designed to be fast. You can generate diffs between revisions, or jump back in time within seconds.

Apache Storm and Mercurial are primarily classified as "Stream Processing" and "Version Control System" tools respectively.

"Flexible" is the top reason why over 7 developers like Apache Storm, while over 18 developers mention "A lot easier to extend than git" as the leading cause for choosing Mercurial.

Apache Storm is an open source tool with 6.23K GitHub stars and 4.07K GitHub forks. Here's a link to Apache Storm's open source repository on GitHub.

AO.com, Bitbucket, and Yomali are some of the popular companies that use Mercurial, whereas Apache Storm is used by Spotify, Twitter, and trivago. Mercurial has a broader approval, being mentioned in 47 company stacks & 150 developers stacks; compared to Apache Storm, which is listed in 57 company stacks and 110 developer stacks.

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Pros of Apache Storm
Pros of Mercurial
  • 10
    Flexible
  • 6
    Easy setup
  • 3
    Clojure
  • 3
    Event Processing
  • 2
    Real Time
  • 18
    A lot easier to extend than git
  • 17
    Easy-to-grasp system with nice tools
  • 13
    Works on windows natively without cygwin nonsense
  • 11
    Written in python
  • 9
    Free
  • 8
    Fast
  • 6
    Better than Git
  • 6
    Best GUI
  • 4
    Better than svn
  • 2
    Hg inc
  • 2
    Good user experience
  • 2
    TortoiseHg - Unified free gui for all platforms
  • 2
    Consistent UI
  • 2
    Easy-to-use
  • 2
    Native support to all platforms
  • 1
    Free to use

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Cons of Apache Storm
Cons of Mercurial
    Be the first to leave a con
    • 0
      Track single upstream only
    • 0
      Does not distinguish between local and remote head

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    - No public GitHub repository available -

    What is Apache Storm?

    Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

    What is Mercurial?

    Mercurial is dedicated to speed and efficiency with a sane user interface. It is written in Python. Mercurial's implementation and data structures are designed to be fast. You can generate diffs between revisions, or jump back in time within seconds.

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

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    Blog Posts

    Mar 4 2020 at 5:14PM

    Atlassian

    GitBitbucketWindows+4
    3
    818
    What are some alternatives to Apache Storm and Mercurial?
    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.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    Amazon Kinesis
    Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
    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.
    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.
    See all alternatives