Stop Thinking, Just Do!

Sung-Soo Kim's Blog

11 SQL-on-Hadoop Tools


15 April 2014

Article Source

11 SQL-on-Hadoop Tools

In our recent post, 8 SQL-on-Hadoop challenges, we quickly listed several tools that help to bridge the gap between the two technologies without going into details. This time we’ll dive in and learn about 11 tools that bring SQL to Hadoop in various ways.

Open Source SQL-on-Hadoop Tools

Apache Hive

Initially developed by Facebook, Apache Hive is a data warehouse infrastructure that is built on top of Hadoop. It allows querying data stored on HDFS for analysis via HQL, an SQL-like language that is translated to MapReduce jobs. Although it seems to provide SQL functionality, Hive still runs jobs on Hadoop as batch processing and does not provide interactive querying. It stores metadata in a relational database and requires maintaining a schema for the data. Only four file formats are supported by Hive: text, SequenceFile, ORC and RCFile. Hive supports processing compressed on Hadoop and also user defined functions.

Bottom line - batch processing on Hadoop with an SQL like language

Apache Sqoop

Apache Sqoop allows importing and exporting data from relational databases to Hadoop via JDBC, the standard API for connecting to databases with Java. It can also work without JDBC as long as the relevant tools allow bulk import/export of data. Sqoop works by running a query on the relational database and exporting the resulting rows into files in either one of these formats: text, binary, Avro, or Sequence Files. These files can then be saved on Hadoop’s HDFS. They can also be exported from Hadoop back into a relational database. Finally, Sqoop integrates with HCatalog, a table and storage management service for Hadoop that allows querying Sqoop’s imported files via Hive or Pig. See our Sqoop blog post for more info.

Bottom line - import/export data from SQL databases to/from Apache Hadoop



BigSQL is a pre-made package of PostgreSQL and Hadoop that you can easily download and install to try out on your local machine. Aside from Apache Hadoop and PostgreSQL, it also includes Cassandra, Tez, Hive, Zookeeper, and HadoopFDW. Extra components such as Pig, Sqoop, and HBase can be downloaded additionally.

Bottom line - pre-made package for trying out Hadoop with PostgreSQL on your machine


While other tools provide SQL-like syntax, Cascading’s Lingual claims to provide a full ANSI SQL interface for Hadoop, thus allowing for easier integration with existing BI tools and helping SQL skilled personnel to use Hadoop immediately. Lingual supports JDBC and also includes an SQL shell. Despite the SQL interface, it still executes queries on Hadoop in batch processing.

Bottom line - ANSI SQL interface for Hadoop

Apache Phoenix

Apache Phoenix is an SQL skin for interactive queries over HBase. It compiles SQL queries into a series of HBase scans and produces JDBC result sets. Note that it requires maintaining a schema which could be built from scratch or mapped from an existing HBase table. Furthermore, there are several features Phoenix doesn’t support: full transaction support, derived tables, relational operators, and misc built-in functions (although they can be added manually). The project is mainly maintained by Salesforce, Intel, and Hortonworks.

Bottom line - interactive SQL over HBase

(Image by JanneM, Some rights reserved)


Cloudera’s Impala is a query engine that runs on top of Hadoop and executes interactive SQL queries on HDFS and HBase. While Hive runs in batch processing, Impala runs the queries in real-time, thus integrating SQL based business intelligence tools with Hadoop. Although Cloudera is the main developer behind this tool, it is fully open source and supports the following file formats:  text, LZO, SequenceFile, Avro and RCFile. Impala can also run on the cloud via Amazon’s Elastic MapReduce.

Bottom line - Cloudera’s solution for interactive SQL queries over HDFS and HBase



Prestois also an interactive SQL query engine. It runs on top of Hive, HBase, and even relational databases and proprietary data stores, thus combining data from multiple sources across the organization. Facebook is the main developer behind Presto and the company uses it to query internal data stores, including a 300PB data warehouse. Airbnb and Dropbox also use Presto, so it seems tried and tested for the enterprise.

Bottom line - Facebook’s solution for interactive SQL queries over Hive and HBase


CitusDB (not to be confused with CitrusDB) is another interactive querying engine with SQL-like functionality that works over Hadoop. It’s based on Dremel, Google’s version of a real-time analytics database to process Big Data, and unlike Impala and Presto it uses PostgreSQL as the SQL engine that works behind the scenes. CitusDB can run on-premise or in the cloud and supports features such as full-text search and geo search as well as ODBC/JDBC compatibility. However, being an analytical database it only supports loading the data in batches.

Bottom line - SQL on Hadoop interactive querying with PostgreSQL

Commercial SQL-on-Hadoop Tools


Hadapt is a commercial product that brings a native SQL implementation to Hadoop. Because it combines Hadoop with a storage layer of a relational database, it allows querying Hadoop via SQL interactively rather than as a batch process. They can handle structured and unstructured data without a predefined schema.

Bottom line - interactive SQL querying on Hadoop


Jethro claims the title of “fastest SQL on Hadoop” by providing an SQL engine for Hadoop that automatically indexes the data as soon as it is written to Hadoop. According to them, it executes queries 100 times faster than Hive and 10 times faster than Impala. Jethro can be added to an existing Hadoop cluster and is supposed to be non-intrusive and it isn’t installed on any of the Hadoop storage nodes.

Bottom line - fast non-intrusive SQL-on-Hadoop via auto-indexing


HAWQ (HAdoop With Query) is a commercial SQL-on-Hadoop platform by Pivotal, a subsidiary of EMC. It provides a parallel SQL query engine using Pivotal’s Greenplum Analytic Database and Hadoop’s HDFS for data storage. This engine is supposed to be useful for analytics with full transaction support and supports creating external tables on HDFS that read text, Hive, HBase, and soon Parquet. Pivotal received some criticism about a year ago that this is not a true Hadoop product because they claim to have over 300 engineers working on Hadoop, yet none of them contribute to any of the Hadoop related projects. As these lines are written, that’s still true.

Bottom line - Pivotal’s SQL-on-Hadoop


[1] Saggi Neumann, 11 SQL-on-Hadoop Tools.

comments powered by Disqus