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Introduction to Greenplum
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Introduction to Greenplum
1.
1© 2016 Pivotal
Software, Inc. All rights reserved. Introduction to Greenplum Database January, 2016
2.
2© 2016 Pivotal
Software, Inc. All rights reserved. Forward Looking Statements This presentation contains “forward-looking statements” as defined under the Federal Securities Laws. Actual results could differ materially from those projected in the forward-looking statements as a result of certain risk factors, including but not limited to: (i) adverse changes in general economic or market conditions; (ii) delays or reductions in information technology spending; (iii) the relative and varying rates of product price and component cost declines and the volume and mixture of product and services revenues; (iv) competitive factors, including but not limited to pricing pressures and new product introductions; (v) component and product quality and availability; (vi) fluctuations in VMware’s Inc.’s operating results and risks associated with trading of VMware stock; (vii) the transition to new products, the uncertainty of customer acceptance of new product offerings and rapid technological and market change; (viii) risks associated with managing the growth of our business, including risks associated with acquisitions and investments and the challenges and costs of integration, restructuring and achieving anticipated synergies; (ix) the ability to attract and retain highly qualified employees; (x) insufficient, excess or obsolete inventory; (xi) fluctuating currency exchange rates; (xii) threats and other disruptions to our secure data centers and networks; (xiii) our ability to protect our proprietary technology; (xiv) war or acts of terrorism; and (xv) other one-time events and other important factors disclosed previously and from time to time in the filings EMC Corporation, the parent company of Pivotal, with the U.S. Securities and Exchange Commission. EMC and Pivotal disclaim any obligation to update any such forward-looking statements after the date of this release.
3.
3© 2016 Pivotal
Software, Inc. All rights reserved. Ÿ Relational database system for big data Ÿ Mission critical & system of record product with supporting tools and ecosystem Ÿ Fully open source with a global community of developers and users Ÿ Implement world’s leading research in database technology across all components – Optimizer, Query Execution – Transaction Processing, Database Storage, Compression, High Availability – Embedded Programming Languages (Python, R, Java, etc …. ) – In-Database analytics in domains (e.g. Geospatial, Text, Machine Learning, Mathematics, etc …. ) Ÿ Performance tuned for multiple workload profiles – Analytics, long running queries, short running queries, mixed workloads Ÿ Large industrial focused system – Financial, Government, Telecom, Retail, Manufacturing, Oil & Gas, etc……. Greenplum Database Mission & Strategy
4.
4© 2016 Pivotal
Software, Inc. All rights reserved. Ÿ An ambitious project – 10 years in the making – Investment of hundred of millions of dollars – Potential to define a new market and disrupt traditional EDW vendors Ÿ www.greenplum.org – Github code – mailing lists / community engagement – Global project w/ external contributors Ÿ Pivotal Greenplum – Enterprise software distribution & release management – Pivotal expertise – 24-hour global support – 5.0 release in Early Q2 2016 Greenplum Open Source
5.
5© 2016 Pivotal
Software, Inc. All rights reserved. PostgreSQL Compatibility Roadmap • Strategic backport key features from PostgreSQL to Greenplum … JSONB, UUID, Variadic functions, Default function arguments, etc. • Consistent back porting of patches from older PostgreSQL to Greenplum … Initial goal to reach 9.0
6.
6© 2016 Pivotal
Software, Inc. All rights reserved. 6 GPDB Architecture Overview
7.
7© 2016 Pivotal
Software, Inc. All rights reserved. MPP Shared Nothing Architecture Standby Master Segment Host with one or more Segment Instances Segment Instances process queries in parallel Performance Through Segment Instance Parallelism High speed interconnect for continuous pipelining of data processing … Master Host SQL Master Host and Standby Master Host Master coordinates work with Segment Hosts Interconnect Segment Host Segment Instance Segment Instance Segment Instance Segment Instance Segment Hosts have their own CPU, disk and memory (shared nothing) Segment Host Segment Instance Segment Instance Segment Instance Segment Instance node1 Segment Host Segment Instance Segment Instance Segment Instance Segment Instance node2 Segment Host Segment Instance Segment Instance Segment Instance Segment Instance node3 Segment Host Segment Instance Segment Instance Segment Instance Segment Instance nodeN
8.
8© 2016 Pivotal
Software, Inc. All rights reserved. Master Host Master Segment Catalog Query Optimizer Distributed TM DispatchQuery Executor Parser enforces syntax, semantics and produces a parse tree Client Accepts client connections, incoming user requests and performs authentication Parser Master Host
9.
9© 2016 Pivotal
Software, Inc. All rights reserved. Pivotal Query Optimizer Local Storage Master Segment CatalogDistributed TM Interconnect DispatcherQuery Executor Parser Query Optimizer Consumes the parse tree and produces the query plan Query execution plan contains how the query is executed Master Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage
10.
10© 2016 Pivotal
Software, Inc. All rights reserved. Query Dispatcher Local Storage Master Segment CatalogDistributed TM Interconnect Query Optimizer Query Executor Parser Dispatcher Responsible for communicating the query plan to segments Allocates cluster resources required to perform the job and accumulating/ presenting final results Master Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage
11.
11© 2016 Pivotal
Software, Inc. All rights reserved. Query Executor Local Storage Master Segment CatalogDistributed TM Interconnect Query Optimizer Query Dispatcher Parser Query Executor Responsible for executing the steps in the plan (e.g. open file, iterate over tuples) Communicates its intermediate results to other executor processes Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Master Host
12.
12© 2016 Pivotal
Software, Inc. All rights reserved. Interconnect Local Storage Master Segment CatalogDistributed TM Query Optimizer Query Dispatcher Parser Query Executor Interconnect Responsible for serving tuples from one segment to another (motion operations) to perform joins, etc. Uses UDP for optimal performance and scalability Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Master Host
13.
13© 2016 Pivotal
Software, Inc. All rights reserved. System Catalog Local Storage Master Segment Query Executor Distributed TM Interconnect Query Optimizer Query Dispatcher Parser Catalog Stores and manages metadata for databases, tables, columns, etc. Master keeps a copy of the metadata coordinated on every segment host Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Master Host
14.
14© 2016 Pivotal
Software, Inc. All rights reserved. Distributed Transaction Management Local Storage Master Segment Query Executor Catalog Interconnect Query Optimizer Query Dispatcher Parser Distributed TM Segments have their own commit and replay logs and decide when to commit, abort for their own transactions DTM resides on the master and coordinates the commit and abort actions of segments Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Segment Host Segment Instance Local TM Query Executor Catalog Local Storage Segment Instance Local TM Query Executor Catalog Local Storage Master Host
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15© 2016 Pivotal
Software, Inc. All rights reserved. GPDB High Availability Ÿ Master Host mirroring – Warm Standby Master Host ▪ Replica of Master Host system catalogs – Eliminates single point of failure – Synchronization process between Master Host and Standby Master Host ▪ Uses PostgreSQL WAL Replication Ÿ Segment mirroring – Creates a mirror segment for every primary segment ▪ Uses a custom file block replication process – If a primary segment becomes unavailable automatic failover to the mirror
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16© 2016 Pivotal
Software, Inc. All rights reserved. Fault Detection and Recovery Ÿ ftsprobe fault detection process monitors and scans segments and database processes at configurable intervals Ÿ Query gp_segment_configuration catalog table for detailed information about a failed segment ▪ $ psql -c "SELECT * FROM gp_segment_configuration WHERE status='d';" Ÿ When ftsprobe cannot connect to a segment it marks it as down – Will remain down until administrator manually recovers the failed segment using gprecoverseg utility Ÿ Automatic failover to the mirror segment – Subsequent connection requests are switched to the mirror segment
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17© 2016 Pivotal
Software, Inc. All rights reserved. CREATE TABLE Define Data Distributions Ÿ One of the most important aspects of GPDB! Ÿ Every table has a distribution method Ÿ DISTRIBUTED BY (column) – Uses a hash distribution Ÿ DISTRIBUTED RANDOMLY – Uses a random distribution which is not guaranteed to provide a perfectly even distribution Ÿ Explicitly define a column or random distribution for all tables – Do not use the default
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18© 2016 Pivotal
Software, Inc. All rights reserved. DISTRIBUTED BY (column_name) • Use a single column that will distribute data across all segments evenly • For large tables significant performance gains can be obtained with local joins (co-located joins) – Distribute on the same column for tables commonly joined together • Co-located join is performed within the segment – Segment operates independently of other segments • Co-located join eliminates or minimizes motion operations – Broadcast motion or Redistribute motion
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19© 2016 Pivotal
Software, Inc. All rights reserved. Use the Same Distribution Key for Commonly Joined Tables = Distribute on the same key used in the join to obtain local joins Segment 1A Segment 2A customer (c_customer_id) freg_shopper (f_customer_id) customer (c_customer_id) freq_shopper (f_customer_id) = =
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20© 2016 Pivotal
Software, Inc. All rights reserved. Redistribution Motion WHERE customer.c_customer_id = freg_shopper.f_customer_id freq_shopper table is dynamically redistributed on f_customer_id Segment 1A customer (c_customer_id) customer_id =102 freg_shopper (f_trans_number) Segment 2A customer (c_customer_id) customer_id=745 freq_shopper (f_trans_number) customer_id=102 Segment 3A customer (c_customer_id) freq_shopper (f_trans_number) customer_id=745
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21© 2016 Pivotal
Software, Inc. All rights reserved. Broadcast Motion WHERE customer.c_statekey = state.s_statekey The state table is dynamically broadcasted to all segments Segment 1A Segment 2A Segment 3A customer (c_customer_id) state (s_statekey) AK, AL, AZ, CA… customer (c_customer_id) state (s_statekey) AK, AL, AZ, CA… customer (c_customer_id) state (s_statekey) AK, AL, AZ, CA…
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22© 2016 Pivotal
Software, Inc. All rights reserved. Data Distribution: The Key to Parallelism The primary strategy and goal is to spread data evenly across all segment instances. Most important in a MPP shared nothing architecture! 43 Oct 20 2005 12 64 Oct 20 2005 111 45 Oct 20 2005 42 46 Oct 20 2005 64 77 Oct 20 2005 32 48 Oct 20 2005 12 Order Order# Order Date Customer ID 50 Oct 20 2005 34 56 Oct 20 2005 213 63 Oct 20 2005 15 44 Oct 20 2005 102 53 Oct 20 2005 82 55 Oct 20 2005 55
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23© 2016 Pivotal
Software, Inc. All rights reserved. Master Parallel Data Scans Across All Segments SELECT COUNT(*) FROM orders WHERE order_date >= ‘Oct 20 2007’ AND order_date < ‘Oct 27 2007’ 4,423,323 Each Segment Scans Data Simultaneously in Parallel Segment 1A Segment 1B Segment 1C Segment 1D Segment 2A Segment 2B Segment 2C Segment 2D Segment 3A Segment 3B Segment 3C Segment 3D Segment 1A Segment 1B Segment 1C Segment 1D Segment 2A Segment 2B Segment 2C Segment 2D Segment 3A Segment 3B Segment 3C Segment 3D Segments Return ResultsReturn ResultsSend Plan to SegmentsDevelop Query Plan
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24© 2016 Pivotal
Software, Inc. All rights reserved. CREATE TABLE Define Partitioning Ÿ Reduces the amount of data to be scanned by reading only the relevant data needed to satisfy a query – The only goal of partitioning is to achieve partition elimination aka partition pruning Ÿ Is not a substitution for distributions – A good distribution strategy and partitioning that achieves partition elimination unlocks performance magic Ÿ Uses table inheritance and constraints – Persistent relationship between parent and child tables
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25© 2016 Pivotal
Software, Inc. All rights reserved. Segment 1A Segment 1B Segment 1C Segment 1D Segment 2A Segment 2B Segment 2C Segment 2D Segment 3A Segment 3B Segment 3C Segment 3D Segment 1A Segment 1B Segment 1C Segment 1D Segment 2A Segment 2B Segment 2C Segment 2D Segment 3A Segment 3B Segment 3C Segment 3D Segment 1A Segment 1B Segment 1C Segment 1D Segment 2A Segment 2B Segment 2C Segment 2D Segment 3A Segment 3B Segment 3C Segment 3D Distributions and Partitioning SELECT COUNT(*) FROM orders WHERE order_date >= ‘Oct 20 2007’ AND order_date < ‘Oct 27 2007’ & Evenly distribute orders data across all segments Only scans the relevant order partitions Segment 1A Segment 1B Segment 1C Segment 1D Segment 2A Segment 2B Segment 2C Segment 2D Segment 3A Segment 3B Segment 3C Segment 3D
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26© 2016 Pivotal
Software, Inc. All rights reserved. Define the Storage Model CREATE TABLE Ÿ Heap Tables versus Append Optimized (AO) Tables Ÿ Row oriented storage versus Column oriented storage Ÿ Compression – Table level compression applied to entire table – Column level compression applied to a specific column w/ columnar storage – Zlib level with Run Length Encoding Optional
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27© 2016 Pivotal
Software, Inc. All rights reserved. Heap Tables or AO Tables • Use heap for tables and partitions that will receive singleton UPDATE, DELETE and INSERT operations • Use heap storage for tables and partitions that will receive concurrent UPDATE, DELETE and INSERT operations • Use AO for tables and partitions that are updated infrequently after the initial load and subsequent inserts or updates are only performed in large batch operations
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28© 2016 Pivotal
Software, Inc. All rights reserved. GPDB Data Loading Options Loading Method Common Uses Examples INSERTS • Operational Workloads • OBDC/JDBC Interfaces INSERT INTO performers (name, specialty) VALUES (‘Sinatra’, ‘Singer’); COPY • Quick and easy data in • Legacy PostgreSQL applications • Output sample results from SQL statements COPY performers FROM ‘/tmp/comedians.dat’ WITH DELIMITER ‘|’; External Tables • High speed bulk loads • Parallel loading using gpfdist protocol • Local file, remote file, HTTP or HDFS based sources INSERT INTO craps_bets SELECT g.bet_type , g.bet_dttm , g.bt_amt FROM x_allbets b JOIN games g ON ( g.id = b.game_id ) WHERE g.name = ‘CRAPS’; GPLOAD • Simplifies external table method (YAML wrapper ) • Supports Insert, Merge & Update gpload –f blackjack_bets.yml
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29© 2016 Pivotal
Software, Inc. All rights reserved. Example Load Architectures Master Host Segment Host Segment Host Segment Host ETL Host Data file Data file Data file Data file Data file Data file gpdfdist gpdfdist ETL Host Data file Data file Data file Data file Data file Data file gpdfdist gpdfdist Segment Instance Segment Instance Segment Instance Segment Instance Segment Instance Segment Instance Master Instance Segment Host Segment Instance Segment Instance Singleton INSERT statement COPY statement INSERT via external table or gpload
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30© 2016 Pivotal
Software, Inc. All rights reserved. Load Using Regular External Tables Ÿ File based (flat files) – gpfdist provides the best performance =# CREATE EXTERNAL TABLE ext_expenses (name text, date date, amount float4, category text, description text) LOCATION ( ‘gpfdist://etlhost:8081/*.txt’, ‘gpfdst://etlhost:8082/*.txt’) FORMAT ’TEXT' (DELIMITER ‘|’ ); $ gpfdist –d /var/load_files1/expenses –p 8081 –l /home/gpadmin/log1 & $ gpfdist –d /var/load_files2/expenses –p 8082 –l /home/gpadmin/log2 &
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31© 2016 Pivotal
Software, Inc. All rights reserved. ANALYZEDB and Database Statistics • Accurate statistics are critical for the query optimizer to generate optimal query plans – When a table is analyzed table information about the data is stored into system catalog tables • Always update statistics after loading data • Always update statistics after CREATE INDEX operations • Always update statistics after INSERT, UPDATE and DELETE operations that significantly changes the underlying data
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32© 2016 Pivotal
Software, Inc. All rights reserved. ANALYZEDB Parallel ANALYZE sessions • Invoke concurrent ANALYZE sessions • Each session is at individual table/partition level • For example: analyzedb -d myDB -t public.big_fact_table -p 4 • Parallel level p between 1 and 10. Default value 5. • Tune parallel level according to system load • In general, 3~5x speed up over single session
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33© 2016 Pivotal
Software, Inc. All rights reserved. ANALYZEDB Incremental ANALYZE • If a table/partition has not changed (DML, DDL) since last run of ANALYZEDB, it will be skipped automatically • ANALYZEDB keeps a record of which tables have up-to-state stats after a run on disk in $MASTER_DATA_DIRECTORY/db_analyze • ANALYZEDB compares the current catalog with the state files of last run to determine the incremental • ANALYZEDB captures statistics on root partition table required for the Pivotal Query Optimizer (PQO)
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34© 2016 Pivotal
Software, Inc. All rights reserved. ANALYZEDB Details • Incremental analyze does not apply to heap tables • Heap tables are always analyzed • Catalog tables, views and external tables are automatically skipped
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35© 2016 Pivotal
Software, Inc. All rights reserved. ANALYZEDB Miscellaneous • Gently kill analyzedb by Ctrl+C or sending SIGINT – it will resume at where it left off when restarted • Print out progress report while running • Refresh root partition stats for the Pivotal Query Optimizer automatically • Analyze tables in descending OID order • Use analyzedb -? for other options (using config file, include/ exclude columns, dry run, force non-incremental, quiet mode)
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36© 2016 Pivotal
Software, Inc. All rights reserved. Greenplum source code major differences w/ PostgreSQL https://github.com/greenplum-db/gpdb/tree/master/gpMgmt Python cluster management code https://github.com/greenplum-db/gpdb/tree/master/gpAux/gpperfmon Performance and system management code https://github.com/greenplum-db/gpdb/tree/master/src/backend/access/appendonly Append-optimized and columnar tables https://github.com/greenplum-db/gpdb/tree/master/src/backend/access/external External tables https://github.com/greenplum-db/gpdb/tree/master/src/backend/cdb Main cluster database code, such as mirroring etc https://github.com/greenplum-db/gpdb/tree/master/src/backend/cdb/motion Interconnect between nodes
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37© 2016 Pivotal
Software, Inc. All rights reserved. 37 Live Demo
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38© 2016 Pivotal
Software, Inc. All rights reserved. Core Greenplum Engine UDP Interconnect Flow Control Roadmap • Replicated Tables; High Performance Temp Tables • Faster Query Dispatch; Short Query Performance • Query Plan Code Generation • Small Material Aggregates • Refactor Analyze for Performance Gains
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39© 2016 Pivotal
Software, Inc. All rights reserved. Polymorphic Storage™ User Definable Storage Layout Ÿ Columnar storage compresses better Ÿ Optimized for retrieving a subset of the columns when querying Ÿ Compression can be set differently per column: gzip (1-9), quicklz, delta, RLE Ÿ Row oriented faster when returning all columns Ÿ HEAP for many updates and deletes Ÿ Use indexes for drill through queries TABLE ‘SALES’ Jun Column-orientedRow-oriented Oct Year -1 Year -2 External HDFS Ÿ Less accessed partitions on HDFS with external partitions to seamlessly query all data Ÿ Text, CSV, Binary, Avro, Parquet format Ÿ All major HDP Distros Nov DecJul Aug Sep Roadmap • GPHDFS Predicate Pushdown • S3 Object Store External Tables • GPDB to GPDB External Tables • HAWQ External Tables
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40© 2016 Pivotal
Software, Inc. All rights reserved. Pivotal Greenplum Roadmap Highlights ● S3 External Tables ● Performance tuned for AWS ● Dynamic Code Generation using LLVM ● Short running query performance enhancements ● Faster analyze ● WAL Replication Segment Mirroring ● Incremental restore MVP ● Disk space full warnings ● Snapshot Backup ● Anaconda Python Modules: NLTK, etc ● Time Series Gap Filling ● Complex Numbers ● PostGIS Raster Support ● Geospatial Trajectories ● Path analytics ● Enhanced SVM module ● Py-Madlib ● Lock Free Backup
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41© 2016 Pivotal
Software, Inc. All rights reserved. • Government detection of benefits that should not be made • Government detection of tax fraud • Government economic statistics research database • Commercial banking wealth management data science and product development • Commercial clearing corporation's risk and trade repositories reporting • Pharmaceutical company vaccine potency prediction based on manufacturing sensors • 401K providers analytics on investment choices • Auto manufacturer’s analytics on predictive maintenance • Corporate/Financial internal email and communication surveillance and reporting • Oil drilling equipment predictive maintenance • Mobile telephone company enterprise data warehouse • Retail store chain customer purchases analytics • Airlines loyalty program analytics • Telecom company network performance and availability analytics • Corporate network anomalous behavior and intrusion detections • Semiconductor Fab sensor analytics and reporting Highlighted Greenplum successes
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