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Why Choose TabbyDB?
With TabbyDB, you can expect not only a resolution to existing issues but also a significant boost in performance for your complex queries. Experience the difference today! If you are facing any functional or performance bug in core spark or sql layer, we would be glad to solve the issue , for your apache spark code base too. Just drop an email!
Constraint
Propagation
Constraint Propagation Rule, describes the new Constraint Propagation algorithm, which solves the issue of Constraints Blow Up due to permutational nature of the logic in stock Spark.
Capping
Query
Capping the Query Plan size, describes the idea of collapsing the project nodes (analysis phase), there by preventing extremely large tree sizes for query plans created using Data Frame APIs
Pushdown
Broadcasted
Pushdown of Broadcasted Keys as runtime filters, Describes the runtime performance enhancement by utilizing the broadcasted keys as filters for file pruning, in case of non parttioned columns used as join keys
TPCDS
Beanchmark
TPCDS benchmark details, including breakdown of time taken, how the benchmark was conducted, configuration used for the tests, and key observations highlighting performance improvements.
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- *Instructions
- The trial version of TabbyDB will be valid for approx 3 months
- The maximum number of executors that can be spawned is limited to 8
- There are two ways to test out TabbyDB
- Download and install TabbyDB version 4.0.1 as a fresh install, the way its done for apache spark.
- This install is 100% compatible with Spark 4.0.1 .
- Get the toolbar by sharing your email. We’ll send you a secure download link.
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- 1. Convert existing Apache Spark 4.0.1 release installed on your machine to TabbyDB
- 2. From your existing spark 4.0.1 install location i.e /jars, remove the following jars and put it in a separate backup location
- spark-catalyst_2.13-4.0.1.jar
- spark-connect_2.13-4.0.1.jar
- spark-hive_2.13-4.0.1.jar
- spark-sql-api_2.13-4.0.1.jar
- spark-common-utils_2.13-4.0.1.jar
- spark-core_2.13-4.0.1.jar
- spark-repl_2.13-4.0.1.jar
- spark-sql_2.13-4.0.1.jar
- 3. Unzip the downloaded tabbydb-jars.tar.gz and add the 8 jars it in the location /jars
- The downloaded TabbyDB jars are compatible with Spark 4.0.1 and in most cases, above steps are sufficient. In case you are using Spark Connect , then you also need to download the TabbyDB's connect client.
Remove any conf property, if present, which excludes any rule of spark, Run your complex use cases and check for performance gains!
If you have any questions/issues please contact at asif.shahid@kwikquery.com
Frequently asked questions
Find clear answers to common questions about TabbyDB’s capabilities, performance, and integration to help you make the most of our advanced query engine.
TabbyDB is a specialized fork of Apache Spark designed to optimize complex queries. It significantly reduces compilation time and memory usage for queries with nested joins, complex case statements, and large query trees through intelligent compile-time and runtime enhancements.
Yes, TabbyDB is built to manage vast and intricate query structures. Its optimizations improve both the speed and resource consumption, enabling faster execution of queries that would typically take hours to compile and run.
TabbyDB maintains full compatibility with Apache Spark’s DataFrame APIs, allowing corporate users to continue using their programmatic query methods while benefiting from enhanced performance without changing their existing codebase.
