The Algorithmics of Write Optimization
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- Godwin Edwards
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1 The Algorithmics of Write Optimization Michael A. Bender Stony Brook University
2 Birds-eye view of data storage incoming data queries stored data answers
3 Birds-eye view of data storage? incoming data queries stored data answers Storage systems face a trade-off between the speed of inserts/deletes/updates and the speed of queries.
4 Point of this talk The tradeoff hurts. Parallel computing is about high performance. To get high performance, we need fast access to our data.
5 Point of this talk The tradeoff hurts. Parallel computing is about high performance. To get high performance, we need fast access to our data. How should we organize our stored data?
6 Point of this talk The tradeoff hurts. Parallel computing is about high performance. To get high performance, we need fast access to our data. How should we organize our stored data? This is a datastructural question.
7 How should we organize our stored data?
8 How should we organize our stored data? Like a librarian?
9 How should we organize our stored data? Like a librarian? Fast to find stuff. Requires work to maintain.
10 How should we organize our stored data? Like a librarian? Like a teenager? Fast to find stuff. Requires work to maintain.
11 How should we organize our stored data? Like a librarian? Like a teenager? Fast to find stuff. Requires work to maintain. Fast to add stuff. Slow to find stuff.
12 How should we organize our stored data? Like a librarian? Like a teenager? Fast to find stuff. Requires work to maintain. Indexing Fast to add stuff. Slow to find stuff. Logging
13 How should we organize our stored data? indexing Sort in logical order. logging Sort in arrival order. (1,1) (2,0) (4,3) (5,2) (8,1) Find a key: fast. Insert a key: slower. (5,2) (8,1) (2,0) (1,1) (4,3) Find a key: slow. Insert a key: fast.
14 How should we organize our stored data? indexing Sort in logical order. logging Sort in arrival order. (1,1) (2,0) (4,3) (5,2) (8,1) Find a key: fast. Insert a key: slower. (5,2) (8,1) (2,0) (1,1) (4,3) Find a key: slow. Insert a key: fast.
15 How should we organize our stored data? indexing Sort in logical order. logging Sort in arrival order. (1,1) (2,0) (4,3) (5,2) (8,1) Find a key: fast. Insert a key: slower. (5,2) (8,1) (2,0) (1,1) (4,3) Find a key: slow. Insert a key: fast.
16 How should we organize our stored data? indexing Sort in logical order. logging Sort in arrival order. (1,1) (2,0) (4,3) (5,2) (8,1) Find a key: fast. Insert a key: slower. (5,2) (8,1) (2,0) (1,1) (4,3) Find a key: slow. Insert a key: fast.
17 How should we organize our stored data? indexing Sort in logical order. logging Sort in arrival order. (1,1) (2,0) (4,3) (5,2) (5,2) (8,1) (2,0) (1,1) (8,1) Find a key: fast. Insert a key: slower. or range of keys (4,3) Find a key: slow. Insert a key: fast.
18 Indexing vs logging spans domains & decades The tradeoff comes under many different names and guises: indexing/fast queries SQL databases NoSQL key-value stores file systems logging/fast data ingestion
19 Indexing vs logging spans domains & decades The tradeoff comes under many different names and guises: clustered indexes unclustered indexes indexing/fast queries InnoDB LevelDB SQL databases NoSQL key-value stores file systems MyISAM WiscKey logging/fast data ingestion
20 Indexing vs logging spans domains & decades The tradeoff comes under many different names and guises: clustered indexes unclustered indexes in-place file systems log-structured file systems indexing/fast queries InnoDB LevelDB ext4 SQL databases NoSQL key-value stores file systems MyISAM WiscKey f2fs LFS logging/fast data ingestion
21 Indexing vs logging spans domains & decades indexing/fast queries The tradeoff comes under many different names and guises: clustered indexes unclustered indexes in-place file systems log-structured file systems etc! InnoDB LevelDB ext4 SQL databases NoSQL key-value stores file systems MyISAM WiscKey f2fs LFS logging/fast data ingestion
22 Indexing vs logging: universal data structures question DBs, kv-stores, and file systems are different beasts. But they grapple with the similar data-structures problems. SQL database SQL processing query optimization nosql database key-value operations file system file and directory operations persistent data structure persistent data structure persistent data structure Disk/SSD
23 Indexing vs logging: universal data structures question DBs, kv-stores, and file systems are different beasts. But they grapple with the similar data-structures problems. Similar problems similar solutions SQL database SQL processing query optimization nosql database key-value operations file system file and directory operations persistent data structure persistent data structure persistent data structure Disk/SSD
24 Indexing vs logging: universal data structures question DBs, kv-stores, and file systems are different beasts. But they grapple with the similar data-structures problems. Similar problems similar solutions write-optimization SQL database SQL processing query optimization nosql database key-value operations file system file and directory operations persistent data structure persistent data structure persistent data structure Disk/SSD
25 Indexing vs logging: universal data structures question Some write-optimized data structures can mitigate or overcome the indexing-logging trade-off. At our DB company Tokutek,* we sold open-source write-optimized databases. Since it was sold, we ve built an open-source file system. TokuDB SQL database SQL processing query optimization TokuMX nosql database nosql processing BetrFS file system file and directory operations TokuDB core TokuDB core TokuDB core Disk/SSD *acquired by Percona
26 Indexing vs logging: universal data structures question Some write-optimized data structures can mitigate or overcome the indexing-logging trade-off. At our DB company Tokutek,* we sold open-source write-optimized databases. Since it was sold, we ve built an open-source file system. TokuDB SQL database SQL processing query optimization TokuMX nosql database nosql processing BetrFS file system file and directory operations TokuDB core TokuDB core TokuDB core I ll talk about my experiences using the same data structure to help all three systems. Disk/SSD *acquired by Percona
27 Point of this talk The performance landscape is fundamentally changing. New data structures New hardware
28 Point of this talk The performance landscape is fundamentally changing. New data structures New hardware This has created tons of new research opportunities. For algorithmists/theorists For systems builders
29 Point of this talk The performance landscape is fundamentally changing. New data structures New hardware This has created tons of new research opportunities. For algorithmists/theorists For systems builders There s still lots to do.
30 An algorithmic view of the insert-query tradeoff
31 Performance characteristics of storage
32 Performance characteristics of storage Sequential access is fast.
33 Performance characteristics of storage Sequential access is fast. Random access is slower.
34 A model for I/O performance How computation works: Data is transferred in blocks between RAM and disk. The # of block transfers dominates the running time. Goal: Minimize # of I/Os Performance bounds are parameterized by block size B, memory size M, data size N. B RAM Disk M B Disk-Access Machine (DAM) model [Aggarwal+Vitter 88]
35 I/Os are slow RAM: ~60 nanoseconds per access Disks: ~6 milliseconds per access. Analogy: RAM escape velocity from earth (40,250 kph) disk walking speed of the giant tortoise (0.4 kph)
36 How realistic is the DAM model?
37 How realistic is the DAM model? "All models are wrong, but some are useful [George Box 1978] What is reality anyway?
38 How realistic is the DAM model? "All models are wrong, but some are useful [George Box 1978] Unrealistic in various ways. What is reality anyway?
39 How realistic is the DAM model? "All models are wrong, but some are useful [George Box 1978] Unrealistic in various ways. Great for reasoning about I/O and for high-level design. What is reality anyway?
![How realistic is the DAM model? "All models are wrong, but some are useful [George Box 1978] What is reality anyway?](/docs-images/89/99956630/images/40-0.jpg "Unrealistic in various ways. Great for reasoning about I/O and for high-level design.")
40 How realistic is the DAM model? "All models are wrong, but some are useful [George Box 1978] What is reality anyway? Unrealistic in various ways. Great for reasoning about I/O and for high-level design. You can optimize the model to hone constants.
41 A model for I/O performance How computation works: Data is transferred in blocks between RAM and disk. The # of block transfers dominates the running time. Goal: Minimize # of I/Os Performance bounds are parameterized by block size B, memory size M, data size N. B RAM Disk M B Disk-Access Machine (DAM) model [Aggarwal+Vitter 88]
42 What s the I/O cost for logging? I/O cost for logging. B N 17 Don t Thrash: How to Cache Your Hash in Flash
43 What s the I/O cost for logging? I/O cost for logging. B N 17 Don t Thrash: How to Cache Your Hash in Flash
44 What s the I/O cost for logging? I/O cost for logging. query: scan all blocks O(N/B) B N 17 Don t Thrash: How to Cache Your Hash in Flash
45 What s the I/O cost for logging? I/O cost for logging. query: scan all blocks O(N/B) insert: append to end of log O(1/B) B N 17 Don t Thrash: How to Cache Your Hash in Flash
46 What s the I/O cost for indexing? Q: What s the I/O cost for indexing? A: It depends on the indexing data structure. The classic structure is the B-tree. B Queries: O(logBN) Updates: O(logBN) 18 Don t Thrash: How to Cache Your Hash in Flash
47 What s the I/O cost for indexing? The classic indexing structure is the B-tree. O(logBN) 19 Don t Thrash: How to Cache Your Hash in Flash
48 What s the I/O cost for indexing? The classic indexing structure is the B-tree. O(logBN) Queries: O(logBN) Inserts: O(logBN) 19 Don t Thrash: How to Cache Your Hash in Flash
49 What s the I/O cost for indexing? The classic indexing structure is the B-tree. O(logBN) optimal Queries: O(logBN) Inserts: O(logBN) 19 Don t Thrash: How to Cache Your Hash in Flash
50 What s the I/O cost for indexing? The classic indexing structure is the B-tree. O(logBN) optimal Queries: O(logBN) Inserts: O(logBN) not optimal!! 19 Don t Thrash: How to Cache Your Hash in Flash
51 Beating B-tree Bounds optimal Queries: O(logBN) Inserts: O(logBN) not optimal!!
52 Goal: Inserts that run faster than a B-tree. Queries that don t run slower. Beating B-tree Bounds optimal Queries: O(logBN) Inserts: O(logBN) not optimal!!
53 Start with a regular B-tree Making a B Ɛ tree
54 Making a B Ɛ tree [Brodal, Fagerberg 03] Reduce the fanout. Now the nodes are mostly empty
55 Making a B Ɛ tree Put B 1/2 -sized buffers in each internal node. [Brodal, Fagerberg 03]
56 Inserts and deletes in a B Ɛ tree Inserts + deletes: Send insert/delete messages down from the root and store them in buffers. When a buffer fills up, flush.
57 Inserts and deletes in a B Ɛ tree Inserts + deletes: Send insert/delete messages down from the root and store them in buffers. When a buffer fills up, flush.
58 Inserts and deletes in a B Ɛ tree Inserts + deletes: Send insert/delete messages down from the root and store them in buffers. When a buffer fills up, flush.
59 Inserts and deletes in a B Ɛ tree Inserts + deletes: Send insert/delete messages down from the root and store them in buffers. When a buffer fills up, flush.
60 Inserts and deletes in a B Ɛ tree Inserts + deletes: Send insert/delete messages down from the root and store them in buffers. When a buffer fills up, flush.
61 Inserts and deletes in a B Ɛ tree Inserts + deletes: Send insert/delete messages down from the root and store them in buffers. When a buffer fills up, flush.
62 Inserts and deletes in a B Ɛ tree Inserts + deletes: Send insert/delete messages down from the root and store them in buffers. When a buffer fills up, flush.
63 Inserts and deletes in a B Ɛ tree Inserts + deletes: Send insert/delete messages down from the root and store them in buffers. When a buffer fills up, flush.
64 Inserts and deletes in a B Ɛ tree Inserts + deletes: Send insert/delete messages down from the root and store them in buffers. When a buffer fills up, flush.
65 Inserts and deletes in a B Ɛ tree Inserts + deletes: Send insert/delete messages down from the root and store them in buffers. When a buffer fills up, flush. Deletes are tombstone messages.
66 Difficulty of key searches
67 Difficulty of key searches
68 Difficulty of key searches This is a keynote talk... so note the keys.
69 Search analysis in B Ɛ tree Searches cost O(logBN) Look in all buffers on root-to-leaf path.
70 Search analysis in B Ɛ tree Searches cost O(logBN) Look in all buffers on root-to-leaf path.
71 Insertions analysis in B Ɛ tree Inserts cost O((logBN)/ B) per insert/delete. Each flush cost 1 I/O and flushes B elements. Flush cost per element is 1/ B. There are O(logBN) levels in a tree.
72 Write-optimization [Brodal, Fagerberg 03] insert point query fanout B O (log B N) O (log B N) fanout B 1/2 O logb N p B O (log B N)
73 Write-optimization [Brodal, Fagerberg 03] insert point query fanout B O (log B N) O (log B N) fanout B 1/2 O logb N p B O (log B N) Example: Record size: 128 bytes Node size: 128 KB B: 1024 records Speedup: =16 Inserts are one-to-two orders-of-magnitude faster than in a B-tree
74 Write-optimization [Brodal, Fagerberg 03] insert point query fanout B O (log B N) O (log B N) fanout B 1/2 O logb N p B O (log B N) Example: Record size: 128 bytes Node size: 128 KB B: 1024 records Speedup: =16 Inserts run 1-2 orders of magnitude faster than in a B-tree. Inserts are one-to-two orders-of-magnitude faster than in a B-tree
75 Optimal insertion-search tradeoff curve [Brodal, Fagerberg 03]
76 Optimal Search-Insert Tradeoff [Brodal, Fagerberg 03] Change the fanout to Change the fanout from B 1/2 from to B ε. B 1/2 to B Ɛ.
77 Optimal Search-Insert Tradeoff [Brodal, Fagerberg 03] Change the fanout to from B 1/2 to B ε. insert point query Optimal tradeoff (function of ɛ=0...1) O log1+b " N B 1 " O log 1+B " N 10x-100x faster inserts B-tree (ɛ=1) ɛ=1/2 ɛ=0 O (log B N) logb N O p B log N O B O (log B N) O (log B N) O (log N)
78 Illustration of Optimal Tradeoff [Brodal, Fagerberg 03] insert point query I/O per Point Query Slow Optimal tradeoff O (function of ɛ=0...1) log1+b " N B 1 " B-tree O log 1+B " N Fast Fast I/O per Insert Slow B=10 5, N=10 12 Don t Thrash: How to Cache Your Hash in Flash
79 Illustration of Optimal Tradeoff [Brodal, Fagerberg 03] insert point query I/O per Point Query Slow Optimal tradeoff O (function of ɛ=0...1) Target of opportunity log1+b " N B 1 " Insertions improve by large factors with almost no loss of point-query performance B-tree O log 1+B " N Fast Fast I/O per Insert Slow B=10 5, N=10 12 Don t Thrash: How to Cache Your Hash in Flash
![Fagerberg 03] 36 Don t](/docs-images/89/99956630/images/80-2.jpg "Thrash: How to Cache Your")
80 logging Illustration of Optimal Tradeoff [Brodal, Fagerberg 03] 36 Don t Thrash: How to Cache Your Hash in Flash
81 iibench Insertion Benchmark Write performance on large data (up is good) (up is good) MongoDB MySQL
82 Other WODS
83 Other write-optimized data structures The most famous write-optimized data structure is the log structured merge tree [O'Neil,Cheng, Gawlick, O'Neil 96] Data structures: [O'Neil,Cheng, Gawlick, O'Neil 96], [Buchsbaum, Goldwasser, Venkatasubramanian, Westbrook 00], [Argel 03], [Graefe 03], [Brodal, Fagerberg 03], [Bender, Farach,Fineman,Fogel, Kuszmaul, Nelson 07], [Brodal, Demaine, Fineman, Iacono, Langerman, Munro 10], [Spillane, Shetty, Zadok, Archak, Dixit 11]. [ Systems: BetrFS, BigTable, Cassandra, H-Base, LevelDB, PebblesDB, RocksDB, TokuDB, TableFS, TokuMX. Don t Thrash: How to Cache Your Hash in Flash
84 Other write-optimized data structures The most famous write-optimized data structure is the log structured merge tree [O'Neil,Cheng, Gawlick, O'Neil 96] There are many others (B Ɛ- tree, buffered repository tree, COLA, x-dict, write-optimized skip list). Data structures: [O'Neil,Cheng, Gawlick, O'Neil 96], [Buchsbaum, Goldwasser, Venkatasubramanian, Westbrook 00], [Argel 03], [Graefe 03], [Brodal, Fagerberg 03], [Bender, Farach,Fineman,Fogel, Kuszmaul, Nelson 07], [Brodal, Demaine, Fineman, Iacono, Langerman, Munro 10], [Spillane, Shetty, Zadok, Archak, Dixit 11]. [ Systems: BetrFS, BigTable, Cassandra, H-Base, LevelDB, PebblesDB, RocksDB, TokuDB, TableFS, TokuMX. Don t Thrash: How to Cache Your Hash in Flash
85 Other write-optimized data structures The most famous write-optimized data structure is the log structured merge tree [O'Neil,Cheng, Gawlick, O'Neil 96] There are many others (B Ɛ- tree, buffered repository tree, COLA, x-dict, write-optimized skip list). Write optimization is having a large impact on systems. Data structures: [O'Neil,Cheng, Gawlick, O'Neil 96], [Buchsbaum, Goldwasser, Venkatasubramanian, Westbrook 00], [Argel 03], [Graefe 03], [Brodal, Fagerberg 03], [Bender, Farach,Fineman,Fogel, Kuszmaul, Nelson 07], [Brodal, Demaine, Fineman, Iacono, Langerman, Munro 10], [Spillane, Shetty, Zadok, Archak, Dixit 11]. [ Systems: BetrFS, BigTable, Cassandra, H-Base, LevelDB, PebblesDB, RocksDB, TokuDB, TableFS, TokuMX. Don t Thrash: How to Cache Your Hash in Flash
86
87
88 A write-optimized dictionary (WOD) data structure.
89 A write-optimized dictionary (WOD) data structure. searches like a B-tree
90 A write-optimized dictionary (WOD) data structure. searches like a B-tree but inserts asymptotically faster.
91 A write-optimized dictionary (WOD) data structure. searches like a B-tree but inserts asymptotically faster. WODs beat the tradeoff from the beginning of the talk.
92 Write-optimization in databases
93 ACID-compliant database built on a B Ɛ -tree application SQL database SQL processing query optimization database index (traditionally a B-tree) file system Disk/SSD
94 ACID-compliant database built on a B Ɛ -tree application SQL database SQL processing query optimization database index (traditionally a B-tree) file system Disk/SSD
95 ACID-compliant database built on a B Ɛ -tree SQL database application SQL processing query optimization database index (traditionally a B-tree) Replace the B-tree with a B ɛ -tree. (The B ε -tree must be full-featured and persistent to power a databases.) file system Disk/SSD
96 ACID-compliant database built on a B Ɛ -tree We built a write-optimized SQL databases at our DB company Tokutek. SQL database application SQL processing query optimization database index (traditionally a B-tree) file system Replace the B-tree with a B ɛ -tree. (The B ε -tree must be full-featured and persistent to power a databases.) Disk/SSD
97 ACID-compliant database built on a B Ɛ -tree We built a write-optimized SQL databases at our DB company Tokutek. SQL database application SQL processing query optimization database index (traditionally a B-tree) file system Replace the B-tree with a B ɛ -tree. (The B ε -tree must be full-featured and persistent to power a databases.) Result: indexing runs 10x-100x faster than traditional structures. Disk/SSD
98 Write optimization. What s missing? Everything else Variable-sized rows Concurrency-control mechanisms Multithreading Transactions, logging, ACID-compliant crash recovery Optimizations for the special cases of sequential inserts and bulk loads Compression Backup
99 Write optimization. What s missing? Everything else Variable-sized rows Concurrency-control mechanisms Multithreading Transactions, logging, ACID-compliant crash recovery Optimizations for the special cases of sequential inserts and bulk loads Compression Backup Yeah, but what about transactions?
100 Transactions, logging, ACID-compliant crash recovery Ingredients a regular write-optimized structure a log periodic checkpoints of the WOD Yeah, but what about transactions?
101 Transactions, logging, ACID-compliant crash recovery Ingredients a regular write-optimized structure a log sequential access periodic checkpoints of the WOD Yeah, but what about transactions?
102 Transactions, logging, ACID-compliant crash recovery Ingredients a regular write-optimized structure a log sequential access sequential access if checkpoints are done right periodic checkpoints of the WOD Yeah, but what about transactions?
103 Transactions, logging, ACID-compliant crash recovery Ingredients a regular write-optimized structure a log sequential access sequential access if checkpoints are done right periodic checkpoints of the WOD Yeah, but what about transactions? Result: even with crash recovery and transactional semantics, indexing is 10x-100x faster than traditional structures.
104 Point of this talk WODs change the performance landscape. WODs help in ways that, at first glance, have little to do with fast insertions.
105 Remember the logging/indexing dilemma? TokuDB a company
106 Remember the logging/indexing dilemma? With TokuDB you can index data 10x-100x faster. TokuDB a company
107 Remember the logging/indexing dilemma? With TokuDB you can index data 10x-100x faster. We don t have an insertion bottleneck. We have a query bottleneck. TokuDB a company
108 Remember the logging/indexing dilemma? With TokuDB you can index data 10x-100x faster. In this case, put a rich set of database indexes in your DB schema. We don t have an insertion bottleneck. We have a query bottleneck. TokuDB a company
109 Remember the logging/indexing dilemma? With TokuDB you can index data 10x-100x faster. In this case, put a rich set of database indexes in your DB schema. We don t have an insertion bottleneck. We have a query bottleneck. We can t. TokuDB a company
110 Remember the logging/indexing dilemma? With TokuDB you can index data 10x-100x faster. In this case, put a rich set of database indexes in your DB schema. Why not? We don t have an insertion bottleneck. We have a query bottleneck. We can t. TokuDB a company
111 Remember the logging/indexing dilemma? With TokuDB you can index data 10x-100x faster. In this case, put a rich set of database indexes in your DB schema. We don t have an insertion bottleneck. We have a query bottleneck. We can t. Why not? We cannot get our insertion rate to keep up. TokuDB a company
112 Remember the logging/indexing dilemma? With TokuDB you can index data 10x-100x faster. In this case, put a rich set of database indexes in your DB schema. We don t have an insertion bottleneck. We have a query bottleneck. We can t. Why not? We cannot get our insertion rate to keep up. TokuDB a company Moral: insertion problems often masquerade as query problems.
113 Remember the logging/indexing dilemma? With TokuDB you can index data 10x-100x faster. In this case, put a rich set of database indexes in your DB schema. We don t have an insertion bottleneck. We have a query bottleneck. We can t. Why not? We cannot get our insertion rate to keep up. We can insert data 10x-100x faster. TokuDB a company Moral: insertion problems often masquerade as query problems.
114 Remember the logging/indexing dilemma? With TokuDB you can index data 10x-100x faster. In this case, put a rich set of database indexes in your DB schema. We don t have an insertion bottleneck. We have a query bottleneck. We can t. Why not? We can insert data 10x-100x faster. We cannot get our insertion rate to keep up. Our inserts are fine. Our queries run too slowly. TokuDB a company Moral: insertion problems often masquerade as query problems.
115 Remember the logging/indexing dilemma? With TokuDB you can index data 10x-100x faster. In this case, put a rich set of database indexes in your DB schema. We don t have an insertion bottleneck. We have a query bottleneck. We can t. Why not? We cannot get our insertion rate to keep up. TokuDB We can insert data 10x-100x faster. Just use a richer set of indexes. a company Our inserts are fine. Our queries run too slowly. Moral: insertion problems often masquerade as query problems.
116 Remember the logging/indexing dilemma? With TokuDB you can index data 10x-100x faster. In this case, put a rich set of database indexes in your DB schema. We don t have an insertion bottleneck. We have a query bottleneck. We can t. Why not? We cannot get our insertion rate to keep up. TokuDB We can insert data 10x-100x faster. Just use a richer set of indexes. a company Our inserts are fine. Our queries run too slowly. We wish we could. Moral: insertion problems often masquerade as query problems.
117 The right read optimization is write optimization I/O Load The right index makes queries run fast. WODS can maintain them. Fast writing is a currency we use to make queries faster.
118 WODs force you to reexamine your system design
119 O logb N p B What the world looks like Insert/point query asymmetry Inserts can be fast: K random writes/sec on a disk. Point queries are provably slow: <200 random reads/sec on a disk. Systems are often designed assuming reads and writes have about the same cost. In fact, writing is easier than reading.
120 Systems often assume search cost = insert cost Ancillary search a search with each insert. Insert with uniqueness check is the key is already present? Delete with acknowledgement was a key actually deleted?
121 Systems often assume search cost = insert cost Ancillary search a search with each insert. Insert with uniqueness check is the key is already present? Delete with acknowledgement was a key actually deleted? These ancillary searches throttle insertions down to the performance of B-trees.
122 Systems often assume search cost = insert cost Ancillary search a search with each insert. Insert with uniqueness check is the key is already present? Delete with acknowledgement was a key actually deleted? In a B-tree, the leaf is already fetched, so reading it has no extra cost. In a WOD, it s expensive. These ancillary searches throttle insertions down to the performance of B-trees.
123 How can we get rid of ancillary searches? Write-optimized systems must get rid of or mitigate ancillary searches whenever possible. It s remarkable that uniqueness checking is hard, but ACID compliance is asymptotically easy. We now live with a different model for what s expensive and what s cheap.
124 Using WODs in File Systems (BetrFS, TokuFS, TableFS are examples of write-optimized file systems. I ll talk about BetrFS)
125 Using WODs in File Systems The empirical tradeoff between writing and querying appears in file systems. (BetrFS, TokuFS, TableFS are examples of write-optimized file systems. I ll talk about BetrFS)
126 Logging versus indexing tradeoff in file systems home doc bender local 2.jpg video latex foo.c 1.mp4 a.tex b.tex directory tree
127 Logging versus indexing tradeoff in file systems How should we organize the files on disk? home doc bender local 2.jpg video latex foo.c 1.mp4 a.tex b.tex directory tree
128 Logging versus indexing tradeoff in file systems How should we organize the files on disk? home logical order sequential scans are fast bender local 2.jpg grep -r bar. ls -R. doc video latex foo.c 1.mp4 a.tex b.tex directory tree
129 Logging versus indexing tradeoff in file systems How should we organize the files on disk? home logical order sequential scans are fast bender local 2.jpg grep -r bar. ls -R. doc video update order small writes are fast latex foo.c 1.mp4 a.tex b.tex directory tree
130 Logging versus indexing tradeoff in file systems How should we organize the files on disk? home logical order sequential scans are fast doc bender local 2.jpg grep -r bar. ls -R. Updates are slow. video update order small writes are fast latex foo.c 1.mp4 Scans are slow. a.tex b.tex directory tree
131 Logging versus indexing tradeoff in file systems How should we organize the files on disk? home logical order sequential scans are fast doc bender local 2.jpg grep -r bar. ls -R. Updates are slow. video update order small writes are fast latex foo.c 1.mp4 Scans are slow. a.tex b.tex The empirical tradeoff between writing and querying appears in file systems. directory tree But we no longer have a B-tree to replace with a B ε -tree.
132 Full-path indexing Maintain two WODs, each indexed on the path names. home <path, file metadata> <path, file data> latex foo.c doc bender local 2.jpg video 1.mp4 /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex a.tex b.tex /home/bender/doc/foo.c /home/bender/local /home/bender/doc/foo.c /home/bender/local directory tree File-system operations inserts and range queries.
133 Full-path indexing Maintain two WODs, each indexed on the path names. home <path, file metadata> <path, file data> latex foo.c doc bender local 2.jpg video 1.mp4 /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex a.tex b.tex /home/bender/doc/foo.c /home/bender/local /home/bender/doc/foo.c /home/bender/local directory tree File-system operations inserts and range queries.
134 Full-path indexing Maintain two WODs, each indexed on the path names. home <path, file metadata> <path, file data> latex foo.c doc bender local 2.jpg video 1.mp4 /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex a.tex b.tex /home/bender/doc/foo.c /home/bender/local /home/bender/doc/foo.c /home/bender/local directory tree ls -R File-system operations inserts and range queries.
135 Full-path indexing Maintain two WODs, each indexed on the path names. home <path, file metadata> <path, file data> latex foo.c doc bender local 2.jpg video 1.mp4 /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex a.tex b.tex /home/bender/doc/foo.c /home/bender/local /home/bender/doc/foo.c /home/bender/local directory tree ls -R grep -r File-system operations inserts and range queries.
136 Full-path indexing Maintain two WODs, each indexed on the path names. home <path, file metadata> <path, file data> latex foo.c doc bender local 2.jpg video 1.mp4 /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex a.tex b.tex /home/bender/doc/foo.c /home/bender/local rm /home/bender/doc/foo.c /home/bender/local directory tree File-system operations inserts and range queries.
137 Microwrite and Scan Performance on BetrFs GNU Find Random 4 byte writes put graphs Time (s) 10 1 BetrFS btrfs ext4 xfs zfs Time (s) GiB file, random data 1,000 random 4-byte writes fsync() at end *lower is better [BetrFS: Jannen, Yuan, Zhan, Akshintala, Esmet, Jiao, MiEal, Pandey, Reddy, Walsh, Bender, Farach-Colton, Johnson, Kuszmaul, Porter, FAST 15]
138 Microwrite and Scan Performance on BetrFs GNU Find Random 4 byte writes log scale put graphs Time (s) 10 1 BetrFS btrfs ext4 xfs zfs Time (s) GiB file, random data 1,000 random 4-byte writes fsync() at end *lower is better [BetrFS: Jannen, Yuan, Zhan, Akshintala, Esmet, Jiao, MiEal, Pandey, Reddy, Walsh, Bender, Farach-Colton, Johnson, Kuszmaul, Porter, FAST 15]
139 Full-path indexing Maintain two WODs, each indexed on the path names. a.tex latex b.tex foo.c home doc bender local 2.jpg video 1.mp4 <path, file metadata> <path, file data> /home/bender/doc /home/bender/doc/latex/ /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex /home/bender/doc/latex/b.tex /home/bender/doc/foo.c /home/bender/doc/foo.c /home/bender/local /home/bender/local directory tree Some file-system operations don t seem to map cheaply.
140 Full-path indexing Maintain two WODs, each indexed on the path names. a.tex latex b.tex foo.c home doc bender local 2.jpg video 1.mp4 <path, file metadata> <path, file data> /home/bender/doc /home/bender/doc/latex/ /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex /home/bender/doc/latex/b.tex /home/bender/doc/foo.c /home/bender/doc/foo.c /home/bender/local /home/bender/local directory tree Some file-system operations don t seem to map cheaply. mv
141 Full-path indexing Maintain two WODs, each indexed on the path names. a.tex latex b.tex foo.c home doc bender local 2.jpg video 1.mp4 <path, file metadata> <path, file data> /home/bender/doc /home/bender/doc/latex/ /home/bender/doc /home/bender/doc/latex/ /home/bender/doc/latex/a.tex /home/bender/doc/latex/a.tex /home/bender/doc/latex/b.tex /home/bender/doc/latex/b.tex /home/bender/doc/foo.c /home/bender/doc/foo.c /home/bender/local /home/bender/local directory tree Some file-system operations don t seem to map cheaply. mv These keys change their names. They move to a different place in the order.
142 WODs open questions Moral: how can we make write-optimized data structures that support the richer set of operations needed by the applications? We need more than just insert and delete.
143 Other WODs advantages
144 Other WODs advantages B Ɛ -trees can use bigger nodes than B-trees Better compression Less fragmentation. B Ɛ -trees file systems do not age the way B-tree based file systems do. [Conway, Bakshi, Jiao, Zhan, Bender, Jannen, Johnson, Kuszmaul, Porter, Yuan, Farach-Colton 17] write- optimized
145 Other WODs advantages B Ɛ -trees can use bigger nodes than B-trees Better compression Less fragmentation. B Ɛ -trees file systems do not age the way B-tree based file systems do. [Conway, Bakshi, Jiao, Zhan, Bender, Jannen, Johnson, Kuszmaul, Porter, Yuan, Farach-Colton 17] write- optimized We cannot see this in the DAM model. We need a more refined model.
146 Revisit I/O model to harness full power of write-optimization DAM is realistic enough to make powerful predictions. Some things it doesn t predict, such as aging.
147 Revisit I/O model to harness full power of write-optimization DAM is realistic enough to make powerful predictions. Some things it doesn t predict, such as aging. Technology is changing. I/O speeds are accelerating faster than CPU. Storage technology supports lots of I/Os in parallel.
148 Revisit I/O model to harness full power of write-optimization DAM is realistic enough to make powerful predictions. Some things it doesn t predict, such as aging. Technology is changing. I/O speeds are accelerating faster than CPU. Storage technology supports lots of I/Os in parallel. Need multithreading and lots of parallel I/Os to drive the device to its capacity. Data structures for older storage don t work so well now.
149
150 Summery Slide
151 The traditional search-insert tradeoff can be improved. Summery Slide
152 Summery Slide The traditional search-insert tradeoff can be improved. We should rethink applications and the storage stack given new write-optimized data structures.
153 Summery Slide The traditional search-insert tradeoff can be improved. We should rethink applications and the storage stack given new write-optimized data structures. WODS are accessible. They are teachable in standard curricula.
154 Summery Slide The traditional search-insert tradeoff can be improved. We should rethink applications and the storage stack given new write-optimized data structures. WODS are accessible. They are teachable in standard curricula. We should revisit the performance model. To get performance now we need parallelism everywhere.
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