| commit | 5473305b0fd5a7f6f639b7c7688d91656bb9c245 | [log] [tgz] |
|---|---|---|
| author | Xikui Wang <xkkwww@gmail.com> | Mon Apr 15 11:05:42 2019 -0700 |
| committer | Xikui Wang <xkkwww@gmail.com> | Tue Apr 16 05:50:38 2019 +0000 |
| tree | 2d39b51b6c02ed12a5249701051a2c3df9626fc5 | |
| parent | 09cc89ec8b1c686f5dcb879e6e345a34ff674828 [diff] |
[NO ISSUE][FUN] Fix type inference and casting in UDFs - user model changes: no - storage format changes: no - interface changes: no Details: The current UDF framework handles the argument types in a sloppy way. It takes in the arguments and reads them as the expected data types in the configuration. This could cause exception at runtime when passing in arguments with unexpected datatypes. When setting the arguments, it did a type casting for numeric values only to make sure the int64 from query interface can be evaluated properly. However, this is not robust enough. This patch fixes the type inference for UDFs during the complation time. The ExternalTypeComputer is refactored to return defined data type, and meanwhile checks the argument data types. Also, the IntroduceDynamicTypeCastForExternalFunctionRule is modified to cover the type castings for data types besides record type. Change-Id: I40506fcca3cd8f14bbd6412359683433256c4c1f Reviewed-on: https://asterix-gerrit.ics.uci.edu/3270 Tested-by: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Integration-Tests: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Reviewed-by: Xikui Wang <xkkwww@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/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.