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Fractal Tree Indexes Theory to Practice Percona Live London 2013

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Fractal Tree Indexes Theory to Practice Percona Live London 2013 ® Fractal Tree Indexes Theory to Practice Percona Live London 2013 Tim Callaghan, Tokutek [email protected] @tmcallaghan ® Tuesday, November 12, 13 Ever seen this? IO Utilization Graph, performance is IO limited ® Tuesday, November 12, 13 Who is Tokutek? Tokutek builds high- performance database Developer Interface" software! " TokuDB - storage engine for Storage Engine" MySQL and MariaDB HDD & SSD! TokuMX - storage engine for storage" MongoDB ® Tuesday, November 12, 13 Who am I? • 17 year database consumer • schema design, development, deployment • database administration + infrastructure • mostly Oracle • 5 year database producer • 2 years @ VoltDB • 2+ years @ Tokutek ® Tuesday, November 12, 13 Housekeeping • Feedback is important to me • Ideas for Webinars or Presentations? • Who’s using MongoDB? • Anyone using TokuDB or TokuMX? • Please ask questions ® Tuesday, November 12, 13 Agenda • Why Fractal Tree indexes are cool ® • What they enable in MySQL (TokuDB) ® • What they enable in MongoDB (TokuMX) • Q+A ® Tuesday, November 12, 13 Indexing: B-trees and Fractal Tree Indexes ® Tuesday, November 12, 13 B-trees ® Tuesday, November 12, 13 B-tree Overview - vocabulary Pivots Pointers Internal Nodes - Path to data Leaf Nodes - Actual Data - Sorted ® Tuesday, November 12, 13 B-tree Overview - example 22 10 99 2, 3, 4 10,20 22,25 99 * Pivot Rule is >= ® Tuesday, November 12, 13 B-tree Overview - search “Find 25” 22 10 99 2, 3, 4 10,20 22,25 99 ® Tuesday, November 12, 13 B-tree Overview - insert “Insert 15” 22 10 99 2, 3, 4 10,15,20 22,25 99 ® Tuesday, November 12, 13 B-tree Overview - performance Performance is IO limited when data > RAM, one IO is needed for each insert/update (actually it’s one IO for every index on the table) 22 RAM 10 99 RAM DISK 2, 3, 4 10,20 22,25 99 ® Tuesday, November 12, 13 Fractal Tree Indexes ® Tuesday, November 12, 13 Fractal Tree Indexes message All internal nodes buffer have message buffers message message As buffers overflow, buffer buffer they cascade down the tree Messages are eventually applied to leaf nodes similar to B-trees different than B-trees •store data in leaf nodes •message buffers •use index key for ordering •big nodes (4MB vs. ~16KB) ® Tuesday, November 12, 13 Fractal Tree Indexes - sample data 25 10 99 2,3,4 10,20 22,25 99 Looks a lot like a b-tree! ® Tuesday, November 12, 13 Fractal Tree Indexes - insert insert 15; insert (15) 25 10 99 2,3,4 10,20 22,25 99 • search operations must consider messages along the way • messages cascade down the tree as buffers fill up • they are eventually applied to the leaf nodes, hundreds or thousands of operations for a single IO • CPU and cache are conserved as important data is not ejected ® Tuesday, November 12, 13 Fractal Tree Indexes - other operations add_column(c4 bigint) 25 delete(99) increment(22,+5) ... delete(8) insert (100) delete(2) 10 99 insert (8) 2,3,4 10,20 22,25 99 Lots of operations can be messages! ® Tuesday, November 12, 13 TokuDB Fractal Tree Indexing + MySQL/MariaDB ® Tuesday, November 12, 13 What is TokuDB? • Transactional MySQL Storage Engine - think InnoDB • Available for MySQL 5.5 and MariaDB 5.5 • ACID and MVCC • Free/OSS Community Edition – http://github.com/Tokutek/ft-engine • Enterprise Edition – Commercial support + hot backup Performance + Compression + Agility ® 20 Tuesday, November 12, 13 TokuDB Performance Warning - Benchmarks Ahead! ® Tuesday, November 12, 13 Indexed Insertion Performance • High-performance insert/update/delete for large databases (> RAM) while maintaining indexes * old numbers, now > 25K/sec ® 22 Tuesday, November 12, 13 Sysbench Performance Sysbench read/write workload, > RAM The fastest IO is the one you never have to do (compression) ® 23 Tuesday, November 12, 13 Performance Advantages • Efficient index maintenance, especially secondary indexes • Clustered secondary indexes • Additional copy of the row is stored in the index • No additional IO to get row data from primary key • Think better covering index (all non-indexed columns) • Compression eliminates size concerns • Big blocks = sequential IO for range scans • Basement nodes are always co-located • Multi-threaded bulk loader ® 24 Tuesday, November 12, 13 TokuDB Compression ® Tuesday, November 12, 13 Compression: TokuDB vs. InnoDB • InnoDB compression misses force node splits, which greatly reduces performance – MySQL 5.6 “dynamic padding” (from FB), less cache • Larger block size and flexible on-disk size wins! • Multiple compression algorithms (lzma, quicklz, zlib) • Larger, less frequent writes (much less IO) • Why it matters on spinning disks: – Compressed reads and amortized compressed writes overcome IO limitations • Why it matters on flash/SSD: – Buy less : 250GB * 10x = as 2.5TB) – Large/less frequent writes are flash friendly ® 26 Tuesday, November 12, 13 Compression + IO Reduction • Server was at 90% IO utilization with InnoDB, 10% IO utilization with TokuDB ® 27 Tuesday, November 12, 13 Compression Performance • iiBench benchmark ® 28 Tuesday, November 12, 13 Compression Achieved • log data (extremely compressible) ® 29 Tuesday, November 12, 13 TokuDB Agility ® Tuesday, November 12, 13 The Challenge of MySQL Schema Changes • Common schema changes can take hours in MySQL – Adding, dropping, or expanding a column – Adding an index • And the table is unavailable for writes during the process • As a workaround, people generally – Use a replication slave, then swap with master – Use helper tools: Percona OSC, MySQL 5.6 o These have IO, CPU, RAM consequences ® 31 Tuesday, November 12, 13 Schema Changes Without Downtime • In TokuDB, column add/drop/expand is instantaneous – “it’s just a message” • Indexes can be created in the background while table is fully available – TokuDB just builds the index, it does not rebuild the table (MySQL getting better) ® 32 Tuesday, November 12, 13 TokuMX Fractal Tree Indexing + MongoDB ® Tuesday, November 12, 13 What is TokuMX? • TokuMX = MongoDB with improved storage (Fractal Tree indexes) • Drop in replacement for MongoDB v2.2 applications – Including replication and sharding – Same data model – Same query language – Drivers just work • Open Source – http://github.com/Tokutek/mongo Performance + Compression + Transactions ® Tuesday, November 12, 13 MongoDB Storage db.test.insert({foo:55}) memory mapped heap db.test.ensureIndex({foo:1}) PK index (_id + pointer) Secondary index (foo + pointer) 18 85 4 5555 40 120 (1,ptr5) (4,ptr1), (19,ptr7) (10000,ptr2) (2,ptr5), (50,ptr4) (100,ptr7) (222,ptr3) (12,ptr8) (22,ptr6) The “pointer” tells MongoDB where to look in the heap for the requested document (another IO) ® 35 Tuesday, November 12, 13 TokuMX Storage db.test.insert({foo:55}) memory mapped heap db.test.ensureIndex({foo:1}) PK index (_id + document) Secondary index (foo + _id) 18 85 4 5555 40 120 (1,doc) (4,doc), (19,doc) (10000,doc) (2,4), (22,12) (50,19) (100,10000) (222,1) (12,doc) One less IO per _id lookup, document is clustered in the index ® 36 Tuesday, November 12, 13 TokuMX Performance ® Tuesday, November 12, 13 Performance - Indexed Insertion • 100mm inserts into a collection with 3 secondary indexes ® 38 Tuesday, November 12, 13 Performance - Inserts on Indexed Arrays • Indexed Insertion : Multikey (100 inserts per doc) ® 39 Tuesday, November 12, 13 Performance - Replication • TokuMX replication allows secondary servers to process replication without IO – Simply injecting messages into the Fractal Tree Indexes on the secondary server – The “Hard Work” was done on the primary o Uniqueness checking o Transactional locking o Update effort (read-before-write) – Elimination of replication lag • Your secondaries are fully available for read scaling! – Wasn’t that the point? ® 40 Tuesday, November 12, 13 Performance - Lock Refinement • TokuMX performs locking at the document level – Extreme concurrency! instance MongoDB v2.0 database database MongoDB v2.2 collection collection collection collection document document document document TokuMX document document document document document document ® 41 Tuesday, November 12, 13 Performance - Lock Refinement ® 42 Tuesday, November 12, 13 Performance - Lock Refinement + Reduced IO • Sysbench benchmark (> RAM) ® 43 Tuesday, November 12, 13 Performance - Reduced IO – Indexed insertion benchmark ® 44 Tuesday, November 12, 13 Performance - Clustered Indexes • Clustered secondary indexes • Additional copy of the document is stored in the index • No additional IO to get row data from primary key • Think better covered index (all non-indexed fields) • Good for point queries, great for range scans • Compression eliminates size concerns ® 45 Tuesday, November 12, 13 Performance - Memory Management • Two approaches to memory management – MongoDB = memory-mapped files o Operating system determines what data is important – TokuMX = managed cache o User defined size o TokuMX determines what data is important • Run multiple TokuMX instances on a single server – Each has it’s own fixed cache size ® 46 Tuesday, November 12, 13 TokuMX Compression ® Tuesday, November 12, 13 Compression • MongoDB does not offer compression – Compressed file systems? – Shortened field names? o Remember: each field name is stored in every single document • TokuMX easily achieves 5x-10x compression – Buy less disk or flash – Compressed reads and writes reduce overall IO • TokuMX support 3 compression types – zlib, quicklz, lzma (size vs. speed) – all data is compressed • Use descriptive field names! – They are easy to compress ® 48 Tuesday, November 12, 13 Compression • 31 million documents,
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