| commit | cc6255585dfffd51e3003c46f07b96b7082aa79f | [log] [tgz] |
|---|---|---|
| author | Damien Raude-Morvan <drazzib@drazzib.com> | Sun Mar 29 14:47:48 2020 +0200 |
| committer | GitHub <noreply@github.com> | Sun Mar 29 14:47:48 2020 +0200 |
| tree | 77938f96ad6f156995f1e3b484462fff9cefcb53 | |
| parent | 392b264fdb0da86556a632be5a12d41da3512f49 [diff] |
GORA-649 Replace usage of deprecated API of MongoDB driver (#207) * GORA-649 MongoDBResult: pass `cursor` and `size` as constructor args * GORA-649 MongoDBQuery: Use Filters and Projections helper * GORA-649 MongoStoreParameters: improve Javadoc * GORA-649: Use org.bson.Document as container * GORA-649 MongoFilterUtil: Avoid changing query passed as reference Return an Optional<> with subfilter to apply * GORA-649 Use Codec interface instead of DefaultDBEncoder * GORA-649 Use new MongoDatabase and MongoCollection API * GORA-649 Use com.mongodb.client.MongoClient interface This interface, while similar to the existing com.mongodb.MongoClient class in that it is a factory for com.mongodb.client.MongoDatabase instances, does not support the legacy com.mongodb.DBCollection-based API : http://mongodb.github.io/mongo-java-driver/3.12/whats-new/#new-entry-point * GORA-649 MongoStore#flush is now no-op Remove fsync handling since its deprecated upstream and should be replaced by proper WriteConcern
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.
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.
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:
Gora is provided under Apache License version 2.0. See LICENSE.txt for more details.