| commit | e66346a34776c2386f782904ca3f5ca3fe8d2f1e | [log] [tgz] |
|---|---|---|
| author | Ildar Absalyamov <ildar.absalyamov@gmail.com> | Thu Sep 14 17:23:57 2017 -0700 |
| committer | Ildar Absalyamov <ildar.absalyamov@gmail.com> | Thu Sep 14 21:43:37 2017 -0700 |
| tree | 8f762579965bb3ce18822311dad34dcaa8dad479 | |
| parent | f52bc5882203de6a16f5eaf385f07e8b93bce25b [diff] |
[STO][IDX] Eliminated excess antimatter in LSMBTree - user model changes: no - storage format changes: no - interface changes: no Details: A combination of some LSM operations (e.g. insert+delete) inserts a record into the memory component of LSMBTree and then deletes it right after leaving an antimatter entry. When no flush happens between two operations this "tombstone" entry does not have any purpose and could be eliminated during the flush without changing search semantics. The fix introduces a new bit in record header which tracks if an entry was inserted and then updated in-place. For secondary indexes this will happen only when the record is changed from regular to antimatter. The patch does not introduce changes in storage format because the bit exists only for memory components. In addition the patch refactored *TupleWriters, *TupleWriterFactories, *TupleReferences, *Frames, *FrameFactories to return index-specific types. Change-Id: I12a67eff8431b52d1f9051b793a5a64b15c009e9 Reviewed-on: https://asterix-gerrit.ics.uci.edu/1538 Sonar-Qube: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Reviewed-by: Till Westmann <tillw@apache.org> Tested-by: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Contrib: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Integration-Tests: Jenkins <jenkins@fulliautomatix.ics.uci.edu>
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 documentations to learn the data model, query language, and how to create a cluster instance.