Apache Kudu vs MySQL Performance Analyzer

Apache Kudu

53
168
+ 1
8
MySQL Performance Analyzer

10
44
+ 1
0
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Kudu vs MySQL Performance Analyzer: What are the differences?

Developers describe Kudu 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. On the other hand, MySQL Performance Analyzer is detailed as "MySQL Performance Analyzer by Yahoo". MySQL Performance Analyzer is an open source project for MySQL performance monitoring and analysis.

Kudu can be classified as a tool in the "Big Data Tools" category, while MySQL Performance Analyzer is grouped under "Database Tools".

Kudu and MySQL Performance Analyzer are both open source tools. It seems that MySQL Performance Analyzer with 1.36K GitHub stars and 193 forks on GitHub has more adoption than Kudu with 789 GitHub stars and 263 GitHub forks.

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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 MySQL Performance Analyzer?

      MySQL Performance Analyzer is an open source project for MySQL performance monitoring and analysis.
      What companies use Apache Kudu?
      What companies use MySQL Performance Analyzer?

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      What tools integrate with Apache Kudu?
      What tools integrate with MySQL Performance Analyzer?
      What are some alternatives to Apache Kudu and MySQL Performance Analyzer?
      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.
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