Time Series Data Models and the future of InfluxDB's Query Language

A talk I gave at the Austin Monitoring Meetup about different open source projects' time series data models and some ideas for a new InfluxDB query language.

Transcript

1. Time Series Data Models & the Query Languages That Love Them Paul Dix paul@influxdb.com @pauldix
2. Future of InfluxDB’s Data Model & Query Language
3. CTO & co-founder makers of
4. Founder of NYC Machine Learning Meetup
6. Editor Addison Wesley’s Data & Analytics
7. Recovering Rubyist
8. Data Models
9. Graphite apps.backend.server_01.counters.requests.count
10. Graphite apps.backend.server_01.counters.requests.count Hierarchy
11. Graphite apps.backend.server_01.counters.requests.count Hierarchy
12. Graphite apps.backend.server_01.counters.requests.count Value - double precision float Time - second precision epoch
13. Graphite apps.backend.server_01.counters.requests.count Value - double precision float Time - second precision epoch regular series only!
14. Regular time series t0 t1 t2 t3 t4 t6 t7 Samples at regular intervals
15. Irregular time series t0 t1 t2 t3 t4 t6 t7 Events whenever they come in
16. OpenTSDB sys.cpu.user host=webserver01,cpu=3 1356998400 1.2
17. OpenTSDB sys.cpu.user host=webserver01,cpu=3 1356998400 1.2 Metric
18. OpenTSDB sys.cpu.user host=webserver01,cpu=3 1356998400 1.2 Tags (string key/value pairs)
19. OpenTSDB sys.cpu.user host=webserver01,cpu=3 1356998400 1.2 millisecond precision epoch
20. OpenTSDB sys.cpu.user host=webserver01,cpu=3 1356998400 1.2 Value int64 or float64 (2.4)
21. Prometheus http_requests_total{method="post",code="200"} 1027 1395066363000
22. Prometheus http_requests_total{method="post",code="200"} 1027 1395066363000 Metric
23. Prometheus http_requests_total{method="post",code="200"} 1027 1395066363000 Labels (string key/value pairs)
24. Prometheus http_requests_total{method="post",code="200"} 1027 1395066363000 Value - float64
25. Prometheus http_requests_total{method="post",code="200"} 1027 1395066363000 millisecond precision epoch
26. InfluxDB 1.x cpu,host=serverA,region=west user=23.2,system=54.1 1465839830100400200
27. InfluxDB 1.x cpu,host=serverA,region=west user=23.2,system=54.1 1465839830100400200 Measurement
28. InfluxDB 1.x cpu,host=serverA,region=west user=23.2,system=54.1 1465839830100400200 Tags (string key/value pairs)
29. InfluxDB 1.x cpu,host=serverA,region=west user=23.2,system=54.1 1465839830100400200 Fields (key/value pairs)
30. InfluxDB 1.x cpu,host=serverA,region=west user=23.2,system=54.1 1465839830100400200 float64 value
31. InfluxDB 1.x cpu,host=serverA,region=west foo=23i 1465839830100400200 int64 value
32. InfluxDB 1.x cpu,host=serverA,region=west bar=t 1465839830100400200 bool value
33. InfluxDB 1.x cpu,host=serverA,region=west line=“some text here” 1465839830100400200 string value
34. InfluxDB 1.x cpu,host=serverA,region=west user=23.2,system=54.1 1465839830100400200 nanosecond precision epoch
35. Differences organization data types precision Graphite hierarchical float64 seconds OpenTSDB metric, tags float64 milliseconds Prometheus metric, tags float64 milliseconds InfluxDB 1.x metric, tags, fields float64, int64, bool, string nanoseconds
36. Querying
37. Data Exploration what series do I have
38. Retrieval & Computation raw data, transforms, materialized series, aggregates, samples
39. Organization matters with thousands of series or more
40. Hierarchy Tree!
41. Lookup metrics/measurements OpenTSDB /api/search/lookup?query= (all series) Prometheus {__name__=~“.+”} (all series?) InfluxDB SHOW MEASUREMENTS
42. Lookup tag/label keys OpenTSDB /api/search/lookup?query= (all series) Prometheus {__name__=~“.+”} (all series?) InfluxDB SHOW TAG KEYS
43. Lookup tag/label values OpenTSDB /api/search/lookup?query={host=*} (all series with host?) Prometheus {host=~“.+”} (all series with host?) InfluxDB SHOW TAG VALUES WITH KEY = “host” SHOW TAG VAVUES FROM “cpu” with KEY = “host”
44. Drill Down OpenTSDB /api/search/lookup?query={host=*,service=mysql} (all metrics on the hosts) Prometheus {host=~“.+”,service=mysql} (all metrics on the hosts) InfluxDB SHOW TAG VALUES WITH KEY = “host” WHERE “service” = ‘mysql’
45. Why to care about drill down (faceted search)
46. Why to care about drill down (faceted search)
47. Why to care about drill down (faceted search) select nonstop, LGA, JFK
48. Facets __name__ host service region group …
49. Facets __name__ host service region group … go_goroutines go_memstats_alloc_bytes go_memstats_alloc_bytes_total go_memstats_gc_sys_bytes go_memstats_other_sys_bytes …
50. Facets __name__ host service region group … go_goroutines go_memstats_alloc_bytes go_memstats_alloc_bytes_total go_memstats_gc_sys_bytes go_memstats_other_sys_bytes … host service region group …
51. Facets __name__ host service region group … go_goroutines go_memstats_alloc_bytes go_memstats_alloc_bytes_total go_memstats_gc_sys_bytes go_memstats_other_sys_bytes … host service region group … dynamic hierarchy! (name already selected)
52. Labels/Tags > Hierarchy
53. Up Front Design
54. Powerful Discovery
55. Slicing, dicing, grouping
56. Query Languages Query Language Example Graphite functional target, from, until sumSeries(summarize(water.level.h2o.feet.*, '1hour', 'max')) OpenTSDB http params startTime, endTime, metric, aggregationFunction, filter, functions, expressions Prometheus functional-ish increase(http_requests_total{job=“prometheus”}[5m]) InfluxDB 1.x SQL-ish select mean(system) from cpu where time > now() - 6h group by time(10m)
57. Functional > SQL or API
58. Time series are streams
59. Apply Functions!
60. Selection what series (streams) are we working with?
61. Timing what time range are we interested in?
62. Merging multiple streams into 1
63. Joining /, *, +, -, &, |, ^, filter
64. Partitioning do we slice the stream into blocks of time?
65. Sampling first, last, min, max, filters
66. Transforming time shift, derivative, rate, interpolate
67. Summarizing count, percentile, mean, median, mode, histogram
68. Future InfluxDB!
69. Subject to Change! *disclaimer
70. Requirements • Support InfluxDB 1.x Data Model • Support InfluxDB 1.x QL • Support Prometheus Data Model • Functional Query Language • Rich Query Builder UI • Query Completion CLI • PromQL?
71. InfluxDB 2.0 Data Model • Tags • non-string values? • Value • int64 • uint64 • float64 • bool • string • bytes • Timestamp (nanosecond)
72. No More Measurement!
73. No more fields?! yep, but remember joining and merging!
74. SIMPLE
75. InfluxDB 2.0 Line Protocol system:cpu,region:cpu,host:a,metric:user_idle 23.2 1491675816
76. InfluxDB 2.0 Line Protocol system:cpu,region:cpu,host:a,metric:user_idle 23.2 1491675816 Tags
77. InfluxDB 2.0 Line Protocol system:cpu,region:cpu,host:a,metric:user_idle 23.2 1491675816 Key
78. InfluxDB 2.0 Line Protocol system:cpu,region:cpu,host:a,metric:user_idle 23.2 1491675816 Value
79. InfluxDB 2.0 Line Protocol system:cpu,region:cpu,host:a,metric:user_idle 23.2 1491675816 Separators
80. InfluxDB 2.0 Line Protocol system:cpu,region:cpu,host:a,metric:user_idle 23.2 1491675816 spaces, /, and : must be escaped
81. InfluxDB 2.0 Line Protocol system:cpu,region:cpu,host:a,metric:user_idle 23.2 1491675816 float64 value
82. InfluxDB 2.0 Line Protocol system:cpu,region:cpu,host:a,metric:user_idle 23 1491675816 float64 value
83. InfluxDB 2.0 Line Protocol system:cpu,region:cpu,host:a,metric:user_idle 23 1491675816 time (precision assumed closest to now)
84. InfluxDB 2.0 Line Protocol system:cpu,region:cpu,host:a,metric:user_idle 23 1491675816000000us time (precision specified)
85. InfluxDB 2.0 Line Protocol system:cpu,region:cpu,host:a,metric:user_idle 23.2 2017-04-08T14:23:54Z time (RFC3339Nano)
86. InfluxDB 2.0 Line Protocol name:foo 2i 1491675816 int64 value
87. InfluxDB 2.0 Line Protocol name:foo 2u 1491675816 uint64 value
88. InfluxDB 2.0 Line Protocol name:foo [234, 21, 9, 23, 87, 90, 11, 54] 1491675816 bytes value
89. InfluxDB 2.0 Line Protocol name:foo “it’s a string, yo!” 2017-04-18T14:58:00Z string value
90. InfluxDB 2.0 Line Protocol name:foo f 2017-04-18T14:58:00Z bool value
91. InfluxQL 2.0 functional!
92. f1(f2(f3(f4(streams)))) Lisp?
95. Paul Graham, Rich Hickey
96. D3 d3.select("body") .selectAll("p") .data([4, 8, 15, 16, 23, 42]) .enter().append("p") .text(function(d) { return "I’m number " + d + "!"; });
97. Function Chaining!
98. Series { "id": 24, "meta": { "dataType": "float64", "metricType": "gauge" }, "tagset": { "host": "A", "region": "B" }, "vector": [ {"value":23.2, "epoch":1491499253}, {"value":78.1, "epoch":1491499263, "tagset":{"host":"B"}} ] }
99. Matrix [ { "tagset": { "host": "A", }, "vector": [{"value":23.1, "epoch":1491499253}, {"value":56.2, "epoch":1491499263}] }, { "tagset": { "host": "B" }, "vector": [{"value":23.1, "epoch":1491499253}, {"value":56.2, "epoch":1491499263}] } ]
100. Example database(name:"testdb") .select(criteria:`"host" = 'A' and "system" = 'cpu'`) .range(startOffset:"-1h")
101. Named Parameters database(name:"testdb") .select(criteria:`"host" = 'A' and "system" = 'cpu'`) .range(startOffset:"-1h") Named parameters!
102. Example Queries database(name:"testdb") .select(criteria:`"host" = 'A' and "system" = 'cpu'`) .range(startOffset:"-1h") Wrap strings in back ticks to avoid pesky escaping
103. Database returns Matrix database(name:"testdb") [ { "tagset": { "host": "A", }, "vector": [ {"value":23.1, “epoch":1491499253}, {"value":56.2, "epoch":1491499263}] }, { "tagset": { "host": "B" }, "vector": [ {"value":23.1, “epoch":1491499253}, {"value":56.2, "epoch":1491499263}] } ]
104. Select filters vectors database(name:"testdb") .select(criteria:`"host" = 'A' and "system" = 'cpu'`)
105. Select filters vectors database(name:"testdb") .select(criteria:`"host" = 'A' and "system" = 'cpu'`) Tag keys
106. Select filters vectors database(name:"testdb") .select(criteria:`"host" = 'A' and "system" = 'cpu'`) Tag values
107. Complex Criteria database(name:"testdb") .select(criteria:`”t1” = ‘foo’ AND (“t2” = ‘bar’ OR “t3” = ‘asdf’)`)
108. Criteria Operators • = • != • =~ • !~ • < • > • startsWith • in • notIn
109. What hosts do we have? database(name:"testdb") .values(key:"host") .sort() .limit(n:20)
110. How many hosts? database(name:"testdb") .values(key:”host") .count()
111. // get the cpu load of hosts that have mysql running var db = database(name:"testdb") db.select( criteria:`"system" = 'cpu' and "metric" = 'load' and "host" in #{ db.select(`"service" = 'mysql'`).values(key:"host") }`) .range(startOffset:"-4h")
112. // get the cpu load of hosts that have mysql running var db = database(name:"testdb") db.select( criteria:`"system" = 'cpu' and "metric" = 'load' and "host" in #{ db.select(`"service" = 'mysql'`).values(key:"host") }`) .range(startOffset:"-4h") Variables
113. // get the cpu load of hosts that have mysql running var db = database(name:"testdb") db.select( criteria:`"system" = 'cpu' and "metric" = 'load' and "host" in #{ db.select(`"service" = 'mysql'`).values(key:"host") }`) .range(startOffset:"-4h") string interpolation
114. // get the count in 10m periods in the last 24h from an event stream // and filter that to only include those periods that were 2 sigma above the average var m = database(name:”testdb”).select(criteria:"\"event\" = 'pageview'") .range(startOffset:"-24h") .merge() .window(func:count(),duration:"10m")
115. // get the count in 10m periods in the last 24h from an event stream // and filter that to only include those periods that were 2 sigma above the average var m = database(name:”testdb”).select(criteria:”\”event\” = 'pageview'") .range(startOffset:"-24h") .merge() .window(func:count(),duration:"10m") // this is shorthand for m.stddev.join(op:"*", right:2) var sigma = m.stddev() * 2
116. // get the count in 10m periods in the last 24h from an event stream // and filter that to only include those periods that were 2 sigma above the average var m = database(name:”testdb”).select(criteria:”\”event\” = 'pageview'") .range(startOffset:"-24h") .merge() .window(func:count(),duration:"10m") // this is shorthand for m.stddev.join(op:"*", right:2) var sigma = m.stddev() * 2 // return only the counts 1 sigma above m.filter(exp:"$ > #{sigma}")
117. // return the last hour of time series of the top 10 host cpu utilizations by // their average load over last 10 minutes var topTen = db.select(criteria:”\”metric\” = 'load' and system = 'cpu'") .range(startOffset:"-10m") .mean() .sort(func:first()) .slice(end:10) .values(key:"host") db.select(criteria:”\"metric\" = 'load' and system = 'cpu' and host in #{topTen}") .range(startOffset:"-1h")
118. Functions • interpolate • join • merge • timeShift • window • rate • first, last, min, max, mean, percentile, etc.
119. Public docs PR in two weeks! please to give feedback :)
120. Thank you. Paul Dix paul@influxdb.com @pauldix