| commit | 80225e2c27d77514ecaa774235951187ef524193 | [log] [tgz] |
|---|---|---|
| author | Ali Alsuliman <ali.al.solaiman@gmail.com> | Mon Oct 15 14:17:07 2018 -0700 |
| committer | Ali Alsuliman <ali.al.solaiman@gmail.com> | Mon Oct 15 21:17:44 2018 -0700 |
| tree | b6383ad30f3d1360b46f363d7d34dac3f976ed48 | |
| parent | adfb63361a1808aadb1782aee03acc4d9af8eb0c [diff] |
[ASTERIXDB-2286][COMP][FUN][HYR] Parallel Sort Optimization - user model changes: yes - storage format changes: no - interface changes: yes details: - new plan for sort operation which includes sampling and replicating the stream of data to be sorted. Sort-merge connector is removed from the plan. The sorted result now is in multiple partitions. - new optimization rule to check whether full parallel sort is applicable. - new Forward operator to read the replicated sort input stream and to receive the ouput of the sampling. - new sequential merge connector to merge a globally ordered result residing in multiple partitions (in addition to the connector's partition computer). - "asterix-lang-aql/pom.xml" is changed as a result of refactoring code related to the range map handling. - new private sampling function to generate the range map object (local & global functions) & their type computers. user model changes: - new compiler property is added to enable and disable parallel sort. interface changes: - "ILogicalOperatorVisitor.java" includes Forward Operator. - "ITuplePartitionComputer.java" includes initialize() to enable partitioner to do some initialization. FieldRangePartitionComputerFactory uses it to pick a range map. - "ITuplePartitionComputerFactory.java". createPartitioner() is changed to createPartitioner(IHyracksTaskContext hyracksTaskContext). Context is needed for transferring the range map throught the context. Change-Id: I73e128029a46f45e6b68c23dfb9310d5de10582f Reviewed-on: https://asterix-gerrit.ics.uci.edu/2393 Tested-by: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Contrib: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Integration-Tests: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Reviewed-by: Dmitry Lychagin <dmitry.lychagin@couchbase.com>
AsterixDB is a BDMS (Big Data Management System) with a rich feature set that sets it apart from other Big Data platforms. Its feature set makes it well-suited to modern needs such as web data warehousing and social data storage and analysis. AsterixDB has:
Data model
A semistructured NoSQL style data model (ADM) resulting from extending JSON with object database ideas
Query languages
Two expressive and declarative query languages (SQL++ and AQL) that support a broad range of queries and analysis over semistructured data
Scalability
A parallel runtime query execution engine, Apache Hyracks, that has been scale-tested on up to 1000+ cores and 500+ disks
Native storage
Partitioned LSM-based data storage and indexing to support efficient ingestion and management of semistructured data
External storage
Support for query access to externally stored data (e.g., data in HDFS) as well as to data stored natively by AsterixDB
Data types
A rich set of primitive data types, including spatial and temporal data in addition to integer, floating point, and textual data
Indexing
Secondary indexing options that include B+ trees, R trees, and inverted keyword (exact and fuzzy) index types
Transactions
Basic transactional (concurrency and recovery) capabilities akin to those of a NoSQL store
Learn more about AsterixDB at its website.
To build AsterixDB from source, you should have a platform with the following:
Instructions for building the master:
Checkout AsterixDB master:
$git clone https://github.com/apache/asterixdb.git
Build AsterixDB master:
$cd asterixdb $mvn clean package -DskipTests
Here are steps to get AsterixDB running on your local machine:
Start a single-machine AsterixDB instance:
$cd asterixdb/asterix-server/target/asterix-server-*-binary-assembly/ $./opt/local/bin/start-sample-cluster.sh
Good to go and run queries in your browser at:
http://localhost:19001
Read more documentation to learn the data model, query language, and how to create a cluster instance.