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madhawa-gunasekara GORA-654 Upgrade Lucene store to 8.5.0 java driver
GORA-654 Upgrade Lucene store to 8.5.0 java driver
Latest commit 588cdc4 Mar 31, 2020

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.github/workflows GORA-644 (#201) Mar 13, 2020
bin Updated compile-examples.sh to preserve package-info.java in the exam… Mar 17, 2019
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gora-accumulo Merge pull request #171 from jhnmora000/master Aug 19, 2019
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gora-benchmark GORA-647 Use Testcontainers for MongoDB integration tests (#206) Mar 29, 2020
gora-cassandra Redefine cassandra generated package as a test source Sep 3, 2019
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gora-core Fix typo in DataStoreTestBase. Oct 3, 2019
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gora-dynamodb Fix mapping file name access method in dynamoDBStore Mar 13, 2020
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gora-redis [GORA-527] Redis Datastore (#198) Oct 22, 2019
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pom.xml GORA-654 Upgrade Lucene store to 8.5.0 java driver Mar 31, 2020

README.md

Apache Gora Project

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The Apache Gora open source framework provides an in-memory data model and persistence for big data. Gora supports persisting to column stores, key value stores, document stores and RDBMSs, and analyzing the data with extensive Apache Hadoop MapReduce, Apache Spark, Apache Flink and Apache Pig support.

Why Gora?

Although there are various excellent ORM frameworks for relational databases, data modeling in NoSQL data stores differ profoundly from their relational cousins. Moreover, data-model agnostic frameworks such as JDO are not sufficient for use cases, where one needs to use the full power of the data models in column stores. Gora fills this gap by giving the user an easy-to-use ORM framework with data store specific mappings and built in Apache Hadoop support.

The overall goal for Gora is to become the standard data representation and persistence framework for big data. The roadmap of Gora can be grouped as follows.

  • Data Persistence : Persisting objects to Column stores such as HBase, Cassandra, Hypertable; key-value stores such as Voldermort, Redis, etc; SQL databases, such as MySQL, HSQLDB, flat files in local file system or Hadoop HDFS.

  • Data Access : An easy to use Java-friendly common API for accessing the data regardless of its location.

  • Indexing : Persisting objects to Lucene and Solr indexes, accessing/querying the data with Gora API.

  • Analysis : Accesing the data and making analysis through adapters for Apache Pig, Apache Hive and Cascading

  • MapReduce support : Out-of-the-box and extensive MapReduce (Apache Hadoop) support for data in the data store.

Background

ORM stands for Object Relation Mapping. It is a technology which abstacts the persistency layer (mostly Relational Databases) so that plain domain level objects can be used, without the cumbersome effort to save/load the data to and from the database. Gora differs from current solutions in that:

  • Gora is specially focussed at NoSQL data stores, but also has limited support for SQL databases.

  • The main use case for Gora is to access/analyze big data using Hadoop.

  • Gora uses Avro for bean definition, not byte code enhancement or annotations.

  • Object-to-data store mappings are backend specific, so that full data model can be utilized.

  • Gora is simple since it ignores complex SQL mappings.

  • Gora will support persistence, indexing and anaysis of data, using Pig, Lucene, Hive, etc.

For the latest information about Gora, please visit our website at:

http://gora.apache.org

License

Gora is provided under Apache License version 2.0. See LICENSE.txt for more details.

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