| commit | b09ec2d99277c2fbce0e6d2961aa4ca483963edd | [log] [tgz] |
|---|---|---|
| author | Ali Alsuliman <ali.al.solaiman@gmail.com> | Thu May 09 17:02:50 2019 -0700 |
| committer | Ali Alsuliman <ali.al.solaiman@gmail.com> | Wed May 15 18:13:50 2019 +0000 |
| tree | 21d1807a7632ebbbd33ba169f5110c773958c4a0 | |
| parent | cd01c55b14ead23be7e9f557f285b4a69d5e8008 [diff] |
[ASTERIXDB-2555][RT][COMP] Make hash join use logical comparison - user model changes: no - storage format changes: no - interface changes: no Details: This patch changes the hash join operator to use the join condition to evaluate if tuples are equal when joining. Binary physical comparators have been removed. The join condition evaluator is in TuplePairEvaluator. - extraced TuplePairEvaluatorFactory out of nested loop join class into a separate class so that it is shared among nested loop and hash join. - switched from FrameTuplePairComparator to ITuplePairComparator in in OptimizedHybridHashJoin and InMemoryHashJoin. - moved debugging code from OptimizedHybridHashJoin into a separate class, JoinUtil. - temporarily made the logical comparison of multisets use raw binary comparison instead of returning null until the logic is implemented. - made IBinaryBooleanInspector a functional interface and updated the implementations. - updated record and array test cases to reflect the new behaviour of hash join where logical comparison could produce null. Also, updated sorting, group by and distinct test cases since the input data has been modified. - added two new input files arrays1nulls.adm & arrays2nulls.adm to be used by the open dataset. previous arrays1.adm & arrays2.adm are used by the closed dataset since it cannot accept arrays with null values. Change-Id: If1834967fdd913fdc76003f09636b2450d07cd5e Reviewed-on: https://asterix-gerrit.ics.uci.edu/3387 Contrib: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Integration-Tests: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Tested-by: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Reviewed-by: Dmitry Lychagin <dmitry.lychagin@couchbase.com> Reviewed-by: Murtadha Hubail <mhubail@apache.org>
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/apache-asterixdb-*-SNAPSHOT $./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.