Fractal Tree Indexes are compared to the indexing incumbent, B-trees. The capabilities are then shown what they bring to MySQL (in TokuDB) and MongoDB (in TokuMX). Presented at Percona Live London 2013.

1. ®
Fractal Tree Indexes
Theory to Practice
Percona Live London 2013
Tim Callaghan, Tokutek
tim@tokutek.com
@tmcallaghan
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Tuesday, November 12, 13

2. Ever seen this?
IO Utilization Graph, performance is IO limited
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3. Who is Tokutek?
Tokutek builds highperformance database
software!
TokuDB - storage engine for
MySQL and MariaDB
TokuMX - storage engine for
MongoDB
Developer Interface"
"
Storage Engine"
HDD & SSD!
storage"
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4. 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
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5. Housekeeping
• Feedback is important to me
• Ideas for Webinars or Presentations?
• Who’s using MongoDB?
• Anyone using TokuDB or TokuMX?
• Please ask questions
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6. Agenda
• Why Fractal Tree indexes are cool
• What they enable in MySQL® (TokuDB)
• What they enable in MongoDB® (TokuMX)
• Q+A
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7. Indexing:
B-trees and
Fractal Tree Indexes
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8. B-trees
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9. B-tree Overview - vocabulary
Pivots
Pointers
Internal Nodes Path to data
Leaf Nodes Actual Data Sorted
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10. B-tree Overview - example
22
10
2, 3, 4
10,20
99
22,25
99
* Pivot Rule is >=
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11. B-tree Overview - search
“Find 25”
22
10
2, 3, 4
10,20
99
22,25
99
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12. B-tree Overview - insert
“Insert 15”
22
10
2, 3, 4
10,15,20
99
22,25
99
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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)
RAM
22
10
99
RAM
DISK
2, 3, 4
10,20
22,25
99
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14. Fractal Tree Indexes
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15. Fractal Tree Indexes
message
buffer
message
buffer
All internal nodes
have message
buffers
message
buffer
As buffers overflow,
they cascade down
the tree
Messages are
eventually applied to
leaf nodes
similar to B-trees
•store data in leaf nodes
•use index key for ordering
different than B-trees
•message buffers
•big nodes (4MB vs. ~16KB)
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16. Fractal Tree Indexes - sample data
25
10
2,3,4
10,20
99
22,25
99
Looks a lot like a b-tree!
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17. Fractal Tree Indexes - insert
insert 15;
insert (15)
25
10
2,3,4
•
•
•
•
99
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
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18. Fractal Tree Indexes - other operations
25
delete(8)
delete(2)
insert (8)
2,3,4
10
10,20
add_column(c4 bigint)
delete(99)
increment(22,+5)
...
99
22,25
insert (100)
99
Lots of operations can be messages!
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19. TokuDB
Fractal Tree Indexing +
MySQL/MariaDB
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20. 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
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21. TokuDB Performance
Warning - Benchmarks Ahead!
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22. Indexed Insertion Performance
• High-performance insert/update/delete for large
databases (> RAM) while maintaining indexes
* old numbers, now > 25K/sec
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23. Sysbench Performance
Sysbench read/write workload, > RAM
The fastest IO is the one you never have to do (compression)
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24. 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
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25. TokuDB Compression
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26. 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
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27. Compression + IO Reduction
• Server was at 90% IO utilization with InnoDB,
10% IO utilization with TokuDB
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28. Compression Performance
• iiBench benchmark
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29. Compression Achieved
• log data (extremely compressible)
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30. TokuDB Agility
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31. 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
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32. 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)
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33. TokuMX
Fractal Tree Indexing +
MongoDB
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34. 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
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35. MongoDB Storage
memory mapped heap
db.test.insert({foo:55})
db.test.ensureIndex({foo:1})
PK index (_id + pointer)
Secondary index (foo + pointer)
18
85
4
(1,ptr5)
(4,ptr1),
(12,ptr8)
5555
(19,ptr7)
40
(10000,ptr2)
(2,ptr5),
(22,ptr6)
(50,ptr4)
120
(100,ptr7)
(222,ptr3)
The “pointer” tells MongoDB where to look in the heap for the requested
document (another IO)
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36. TokuMX Storage
db.test.insert({foo:55})
db.test.ensureIndex({foo:1})
memory mapped heap
PK index (_id + document)
Secondary index (foo + _id)
18
4
(1,doc)
(4,doc),
(12,doc)
85
5555
(19,doc)
40
(10000,doc)
(2,4), (22,12)
(50,19)
120
(100,10000)
(222,1)
One less IO per _id lookup, document is clustered in the index
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37. TokuMX Performance
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38. Performance - Indexed Insertion
• 100mm inserts into a collection with 3 secondary indexes
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39. Performance - Inserts on Indexed Arrays
• Indexed Insertion : Multikey (100 inserts per doc)
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40. 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?
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41. Performance - Lock Refinement
• TokuMX performs locking at the document level
– Extreme concurrency!
instance
database
collection
MongoDB v2.0
database
collection collection
collection
document
document document document
document
document
document
MongoDB v2.2
TokuMX
document
document
document
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42. Performance - Lock Refinement
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43. Performance - Lock Refinement + Reduced IO
• Sysbench benchmark (> RAM)
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44. Performance - Reduced IO
– Indexed insertion benchmark
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45. 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
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46. 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
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47. TokuMX Compression
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48. 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
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49. Compression
• 31 million documents, bit torrent peer data
– http://cs.brown.edu/~pavlo/torrent/
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50. TokuMX Transactions
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51. ACID + MVCC
• ACID
– In MongoDB, multi-insertion operations allow for
partial success
o Asked to store 5 documents, 3 succeeded
– We offer “all or nothing” behavior
– Document level locking
• MVCC
– In MongoDB, queries can be interrupted by writers.
o The effect of these writers are visible to the reader
– TokuMX offers MVCC
o Reads are consistent as of the operation start
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52. Multi-statement Transactions
• TokuMX brings the following to MongoDB
– db.runCommand({“beginTransaction”, “isolation”:
“mvcc”})
– ... perform 1 or more operations
– db.runCommand(“rollbackTransaction”) |
db.runCommand(“commitTransaction”)
• Not allowed in sharded environments
– mongos will reject
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53. Questions?
Tim Callaghan
VP/Engineering, Tokutek
tim@tokutek.com
@tmcallaghan
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Tuesday, November 12, 13