| commit | d906bd89e48351156262c1c22096d23269bf0f0a | [log] [tgz] |
|---|---|---|
| author | Taewoo Kim <wangsaeu@yahoo.com> | Mon Oct 08 18:01:23 2018 -0700 |
| committer | Taewoo Kim <wangsaeu@gmail.com> | Tue Oct 09 17:58:47 2018 -0700 |
| tree | a28719e110f2ec6ab9dd2693233e1c0fe650737a | |
| parent | e7fa4b3fb1662f1ae6d1a2336e2be6b51e18f22d [diff] |
[NO ISSUE][COMP][RT] Enable multiway similarity joins
- Enable the FuzzyJoinRule that transforms
a nested-loop-similarity-join plan to a three-stage-similarity join.
- Modify FuzzyJoinRuleCollections.
- Add the ExtractCommonExpressionRule to extract common expressions
in the star-like multiple similarity join substitutions.
- Add the InlineSubplanInputForNestedTupleSourceRule to translate
the generated subplan from the similarity function-derived
substitution into join in case of nested schemas.
- Use similarity-jaccard-prefix to enable the pp+ join strategy.
- Use the right side to build the heavy hash join on
the prefix tokens from both sides.
- Add RemoveAssign/Variables/AggRules to iteratively remove unused
assign/vars once FuzzyJoinRule is applied in each round.
- Add three new optimization cases for multiway similarity joins.
- link-like multiway similarity joins
- star-like multiway similarity joins
- hybrid multiway similarity joins with the both styles of similarity joins.
- Add a check whether a similarity function is on
a select over an existing similarity join.
- Change the inverted-index-based similarity join to the three-stage-similarity join
due to efficiency considerations.
Change-Id: I8736f104905eeda763d39709e002c2b9629278cc
Reviewed-on: https://asterix-gerrit.ics.uci.edu/1076
Sonar-Qube: Jenkins <jenkins@fulliautomatix.ics.uci.edu>
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>
Reviewed-by: Taewoo Kim <wangsaeu@gmail.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.