I. Phoenix

This is a Java middle tier that allows developers to execute SQL queries on Apache HBase.Phoenix is written entirely in Java, the code is located on GitHub, and provides a client-embeddable JDBC driver.

The Phoenix query engine converts an SQL query into one or more HBase scans and orchestrates the execution to produce a standard JDBC result set. Using the HBase API, co-processors and custom filters directly, the performance order of magnitude is milliseconds for simple queries and seconds for millions of rows.

Two, Presto

Facebook's open source data query engine, Presto , can be more than 250 petabytes of data can be analyzed quickly and interactively. The project began development in the fall of 2012, and is now used by over 1,000 Facebook employees running over 30,000 queries with 1PB of data per day. Facebook claims that Presto's performance is more than 10x better than things like Hive and Map*Reduce.

Presto currently supports most ANSI SQL effects, including union queries, left and right joins, subqueries, and a number of aggregation and computation functions; it supports near truncated counts (DISTINCT COUNT), etc.

Presto currently supports most ANSI SQL effects, including union queries, left and right joins, subqueries, and some aggregation and computation functions.

Three, Shark

Shark that is, Hive on Spark, essentially Hive's HQL parsing, HQL translation into RDD operations on Spark, and then through Hive's metadata to get database The feature of Shark is that it is fast, fully compatible with Hive, and can use APIs such as rdd2sql() in shell mode to take the result set obtained from HQL and continue to operate in the scala environment, which supports writing simple machine learning or simple analytical processing functions on HQL. simple analytical processing functions to further analyze and calculate the HQL results.

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