Apache Hive alternatives and similar libraries
Based on the "Database" category.
Alternatively, view Apache Hive alternatives based on common mentions on social networks and blogs.
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MapDB
MapDB provides concurrent Maps, Sets and Queues backed by disk storage or off-heap-memory. It is a fast and easy to use embedded Java database engine. -
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orientdb
OrientDB is the most versatile DBMS supporting Graph, Document, Reactive, Full-Text and Geospatial models in one Multi-Model product. OrientDB can run distributed (Multi-Master), supports SQL, ACID Transactions, Full-Text indexing and Reactive Queries. -
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Crate
CrateDB is a distributed and scalable SQL database for storing and analyzing massive amounts of data in near real-time, even with complex queries. It is PostgreSQL-compatible, and based on Lucene. -
ObjectBox embedded database
Database for Android and JVM - first and fast, lightweight on-device vector database -
Chronicle Map
Replicate your Key Value Store across your network, with consistency, persistance and performance. -
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JDBI
The Jdbi library provides convenient, idiomatic access to relational databases in Java and other JVM technologies such as Kotlin, Clojure or Scala. -
sql2o
sql2o is a small library, which makes it easy to convert the result of your sql-statements into objects. No resultset hacking required. Kind of like an orm, but without the sql-generation capabilities. Supports named parameters. -
JetBrains Xodus
Transactional schema-less embedded database used by JetBrains YouTrack and JetBrains Hub. -
FlexyPool
FlexyPool adds metrics and failover strategies to a given Connection Pool, allowing it to resize on demand. -
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InfluxDB – Built for High-Performance Time Series Workloads
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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README
Apache Hive (TM)
The Apache Hive (TM) data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Built on top of Apache Hadoop (TM), it provides:
Tools to enable easy access to data via SQL, thus enabling data warehousing tasks such as extract/transform/load (ETL), reporting, and data analysis
A mechanism to impose structure on a variety of data formats
Access to files stored either directly in Apache HDFS (TM) or in other data storage systems such as Apache HBase (TM)
Query execution using Apache Hadoop MapReduce or Apache Tez frameworks.
Hive provides standard SQL functionality, including many of the later 2003 and 2011 features for analytics. These include OLAP functions, subqueries, common table expressions, and more. Hive's SQL can also be extended with user code via user defined functions (UDFs), user defined aggregates (UDAFs), and user defined table functions (UDTFs).
Hive users have a choice of 3 runtimes when executing SQL queries. Users can choose between Apache Hadoop MapReduce or Apache Tez frameworks as their execution backend. MapReduce is a mature framework that is proven at large scales. However, MapReduce is a purely batch framework, and queries using it may experience higher latencies (tens of seconds), even over small datasets. Apache Tez is designed for interactive query, and has substantially reduced overheads versus MapReduce.
Users are free to switch back and forth between these frameworks at any time. In each case, Hive is best suited for use cases where the amount of data processed is large enough to require a distributed system.
Hive is not designed for online transaction processing. It is best used for traditional data warehousing tasks. Hive is designed to maximize scalability (scale out with more machines added dynamically to the Hadoop cluster), performance, extensibility, fault-tolerance, and loose-coupling with its input formats.
General Info
For the latest information about Hive, please visit out website at:
Getting Started
Installation Instructions and a quick tutorial: https://cwiki.apache.org/confluence/display/Hive/GettingStarted
Instructions to build Hive from source: https://cwiki.apache.org/confluence/display/Hive/GettingStarted#GettingStarted-BuildingHivefromSource
A longer tutorial that covers more features of HiveQL: https://cwiki.apache.org/confluence/display/Hive/Tutorial
The HiveQL Language Manual: https://cwiki.apache.org/confluence/display/Hive/LanguageManual
Requirements
Java
| Hive Version | Java Version |
|---|---|
| Hive 1.0 | Java 6 |
| Hive 1.1 | Java 6 |
| Hive 1.2 | Java 7 |
| Hive 2.x | Java 7 |
| Hive 3.x | Java 8 |
| Hive 4.x | Java 8 |
Hadoop
- Hadoop 1.x, 2.x
- Hadoop 3.x (Hive 3.x)
Upgrading from older versions of Hive
Hive includes changes to the MetaStore schema. If you are upgrading from an earlier version of Hive it is imperative that you upgrade the MetaStore schema by running the appropriate schema upgrade scripts located in the scripts/metastore/upgrade directory.
We have provided upgrade scripts for MySQL, PostgreSQL, Oracle, Microsoft SQL Server, and Derby databases. If you are using a different database for your MetaStore you will need to provide your own upgrade script.
Useful mailing lists
[email protected] - To discuss and ask usage questions. Send an empty email to [email protected] in order to subscribe to this mailing list.
[email protected] - For discussions about code, design and features. Send an empty email to [email protected] in order to subscribe to this mailing list.
[email protected] - In order to monitor commits to the source repository. Send an empty email to [email protected] in order to subscribe to this mailing list.