InfluxDB's new storage engine: The Time Structured Merge Tree

Transcript

1. InfluxDB’s new storage engine: The Time Structured Merge Tree Paul Dix CEO at InfluxDB @pauldix paul@influxdb.com
2. preliminary intro materials…
3. Everything is indexed by time and series
4. Shards 10/11/2015 10/12/2015 Data organized into Shards of time, each is an underlying DB efficient to drop old data 10/13/2015 10/10/2015
5. InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126
6. InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126 Measurement
7. InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126 Measurement Tags
8. InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126 Measurement Tags Fields
9. InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126 Measurement Tags Fields Timestamp
10. InfluxDB data temperature,device=dev1,building=b1 internal=80,external=18 1443782126 Measurement Tags Fields Timestamp We actually store up to ns scale timestamps but I couldn’t fit on the slide
11. Each series and field to a unique ID temperature,device=dev1,building=b1#internal temperature,device=dev1,building=b1#external 1 2
12. Data per ID is tuples ordered by time temperature,device=dev1,building=b1#internal temperature,device=dev1,building=b1#external 1 2 1 (1443782126,80) 2 (1443782126,18)
13. Arranging in Key/Value Stores 1,1443782126 Key Value 80 ID Time
14. Arranging in Key/Value Stores 1,1443782126 Key Value 80 2,1443782126 18
15. Arranging in Key/Value Stores 1,1443782126 Key Value 80 2,1443782126 18 1,1443782127 81 new data
16. Arranging in Key/Value Stores 1,1443782126 Key Value 80 2,1443782126 18 1,1443782127 81 key space is ordered
17. Arranging in Key/Value Stores 1,1443782126 Key Value 80 2,1443782126 18 1,1443782127 81 2,1443782256 15 2,1443782130 17 3,1443700126 18
18. Many existing storage engines have this model
19. New Storage Engine?!
20. First we used LSM Trees
21. deletes expensive
22. too many open file handles
23. Then mmap COW B+Trees
24. write throughput
25. compression
26. met our requirements
27. High write throughput
28. Awesome read performance
29. Better Compression
30. Writes can’t block reads
31. Reads can’t block writes
32. Write multiple ranges simultaneously
33. Hot backups
34. Many databases open in a single process
35. Enter InfluxDB’s Time Structured Merge Tree (TSM Tree)
36. Enter InfluxDB’s Time Structured Merge Tree (TSM Tree) like LSM, but different
37. Components WAL In memory cache Index Files
38. Components WAL In memory cache Index Files Similar to LSM Trees
39. Components WAL In memory cache Index Files Similar to LSM Trees Same
40. Components WAL In memory cache Index Files Similar to LSM Trees Same like MemTables
41. Components WAL In memory cache Index Files Similar to LSM Trees Same like MemTables like SSTables
42. awesome time series data WAL (an append only file)
43. awesome time series data WAL (an append only file) in memory index
44. In Memory Cache // cache and flush variables cacheLock sync.RWMutex cache map[string]Values flushCache map[string]Values temperature,device=dev1,building=b1#internal
45. In Memory Cache // cache and flush variables cacheLock sync.RWMutex cache map[string]Values flushCache map[string]Values writes can come in while WAL flushes
46. // cache and flush variables cacheLock sync.RWMutex cache map[string]Values flushCache map[string]Values dirtySort map[string]bool values can come in out of order. mark if so, sort at query time
47. Values in Memory type Value interface { Time() time.Time UnixNano() int64 Value() interface{} Size() int }
48. awesome time series data WAL (an append only file) in memory index on disk index (periodic flushes)
49. The Index Data File Min Time: 10000 Max Time: 29999 Data File Min Time: 30000 Max Time: 39999 Data File Min Time: 70000 Max Time: 99999 Contiguous blocks of time
50. The Index Data File Min Time: 10000 Max Time: 29999 Data File Min Time: 15000 Max Time: 39999 Data File Min Time: 70000 Max Time: 99999 can overlap
51. The Index cpu,host=A Min Time: 10000 Max Time: 20000 cpu,host=A Min Time: 21000 Max Time: 39999 Data File Min Time: 70000 Max Time: 99999 but a specific series must not overlap
52. The Index Data File Data File Data File a file will never overlap with more than 2 others time ascending Data File Data File
53. Data files are read only, like LSM SSTables
54. The Index Data File Min Time: 10000 Max Time: 29999 Data File Min Time: 30000 Max Time: 39999 Data File Min Time: 70000 Max Time: 99999 Data File Min Time: 10000 Max Time: 99999 they periodically get compacted (like LSM)
55. Compacting while appending new data
56. Compacting while appending new data func (w *WriteLock) LockRange(min, max int64) { // sweet code here } func (w *WriteLock) UnlockRange(min, max int64) { // sweet code here }
57. Compacting while appending new data func (w *WriteLock) LockRange(min, max int64) { // sweet code here } func (w *WriteLock) UnlockRange(min, max int64) { // sweet code here } This should block until we get it
58. Locking happens inside each Shard
59. Back to the data files… Data File Min Time: 10000 Max Time: 29999 Data File Min Time: 30000 Max Time: 39999 Data File Min Time: 70000 Max Time: 99999
60. Data File Layout
61. Data File Layout Similar to SSTables
62. Data File Layout
63. Data File Layout blocks have up to 1,000 points by default
64. Data File Layout
65. Data File Layout 4 byte position means data files can be at most 4GB
66. Data Files type dataFile struct { f *os.File size uint32 mmap []byte }
67. Memory mapping lets the OS handle caching for you
68. Access file like a byte slice func (d *dataFile) MinTime() int64 { minTimePosition := d.size - minTimeOffset timeBytes := d.mmap[minTimeOffset : minTimeOffset+timeSize] return int64(btou64(timeBytes)) }
69. Binary Search for ID func (d *dataFile) StartingPositionForID(id uint64) uint32 { seriesCount := d.SeriesCount() indexStart := d.indexPosition() min := uint32(0) max := uint32(seriesCount) for min < max { mid := (max-min)/2 + min offset := mid*seriesHeaderSize + indexStart checkID := btou64(d.mmap[offset : offset+timeSize]) if checkID == id { return btou32(d.mmap[offset+timeSize : offset+timeSize+posSize]) } else if checkID < id { min = mid + 1 } else { max = mid } } return uint32(0) } The Index: IDs are sorted
70. Compressed Data Blocks
71. Timestamps: encoding based on precision and deltas
72. Timestamps (best case): Run length encoding Deltas are all the same for a block
73. Timestamps (good case): Simple8B Ann and Moffat in "Index compression using 64-bit words"
74. Timestamps (worst case): raw values nano-second timestamps with large deltas
75. float64: double delta Facebook’s Gorilla - google: gorilla time series facebook https://github.com/dgryski/go-tsz
76. booleans are bits!
77. int64 uses zig-zag same as from Protobufs (also looking at adding double delta and RLE)
78. string uses Snappy same compression LevelDB uses (might add dictionary compression)
79. How does it perform?
80. Compression depends greatly on the shape of your data
81. Write throughput depends on batching, CPU, and memory
82. test last night: 100,000 series 100,000 points per series 10,000,000,000 total points 5,000 points per request c3.8xlarge, writes from 4 other systems ~390,000 points/sec ~3 bytes/point (random floats, could be better)
83. ~400 IOPS 30%-50% CPU There’s room for improvement!
84. Detailed writeup https://influxdb.com/docs/v0.9/concepts/storage_engine.html
85. Thank you! Paul Dix @pauldix paul@influxdb.com