| commit | ed469381235990ce5ecd2f242b679190ef2ca263 | [log] [tgz] |
|---|---|---|
| author | luochen01 <cluo8@uci.edu> | Mon Nov 27 11:14:01 2017 -0800 |
| committer | Luo Chen <cluo8@uci.edu> | Mon Nov 27 13:18:52 2017 -0800 |
| tree | a4f806fe7972490da7e906920d13ea7136eba6b2 | |
| parent | c5a0a1974d36d647a22d606d53bdaafd85f641df [diff] |
[ASTERIXDB-2149] Enable multiple normalized keys in sort - user model changes: no - storage format changes: no - interface changes: yes. The interface of sort is changed. Currently, during the (in-memory) sort, we use an int normalized keys to speed up comparisions by avoiding random memory accesses. However, this technique is inefficient if the first 4 bytes of the sorting keys are not distinctive. From performance point of view, it's better to use longer normalized keys when it's possible (2-3x improvements). This is enabled by this patch by: - Allowing multiple normalized keys during sort, and the length of each normalized key can be longer (multiple integers). - Enable memory budgeting of pointer directories as well during sort (but for performance, we still use int[], instead of byte[] from frame). The next patch will enable the AsterixDB layer to use this feature to speed up sort performance. Change-Id: I4354242ff731b4b006b8446b58f65873047dde78 Reviewed-on: https://asterix-gerrit.ics.uci.edu/2127 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: abdullah alamoudi <bamousaa@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 documentations to learn the data model, query language, and how to create a cluster instance.