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Presto:distributed sql query engine

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12 diapositives · 720 Views · Nov 15, 2013

Presto:distributed sql query engine

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Presto:distributed sql query engine
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  1. PRESTO Kiran Palaka

  2. Problem to solve • Huge production of data. • As data is growing enormously to the point of peta bytes , querying the database has become a big issue. • So we should be able to run more interactive queries and get results faster .

  3. Introduction • Presto is a open source distributed sql query engine. • For running queries against of all sizes ranging from gigabytes to petabytes . • It supports ANSI SQL ,including complex queries,aggresgations,joins and window functions . • It is implemented in java.

  4. Presto: I can query

  5. Architecture

  6. Architecture Explanation • Client sends sql to presto coordinator. • Coordinator parses ,analyzes and plans the query execution. • The scheduler wires together the execution pipeline ,assigns work to nodes closest to data and monitors the progress. • The client pulls the data from output stage which in turn pulls data from underlying stages.

  7. Hive/Mapreduce Execution model • Hive translates queries into multiple stage of mapreduce tasks and execute them one after the other. • Each task reads input from disk and writes intermediate output back to disk.

  8. Presto Execution • Presto engine does not use Mapreduce. • It employs a custom query and execution engine with operators designed to support sql semantics. • Processing is in memory and pipelined across the network between stages which avoids unnecessary I/O and associated latency overhead. • Pipelined execution model runs multiple stages at once and streams data from one stage to next as it becomes available which reduces end-to-end latency

  9. Note • Presto dynamically compiles certain portions of query plan to byte code which lets JVM optimize and generate native machine code.

  10. Extensibility • Presto was designed with a simple storage abstraction that makes its easy to provide sql query capability against disparate data sources. • Connectors only need to provide interfaces for fetching meta data, getting data locations and accessing data itself.

  11. Limitations • Size limitation on the join tables and cardinality of unique groups. • Lacks the ability to write output back to tables. Currently query results are streamed to client.

  12. Presto developers claim: • Presto is 10x better than hive/Mapreduce in terms of cpu efficiency and latency for most queries. • Supports ANSI sql, including joins, left/right outer joins,subqueries,most of the common aggregate and scalar functions, including approximate distinct counts, approximate percentiles