Flux and InfluxDB 2.0

Talk given at InfluxDays SF 2018 on Flux, the new language we're creating, and where we're going with InfluxDB 2.0.

Transcript

1. Flux and InfluxDB 2.0 Paul Dix @pauldix paul@influxdata.com
3. • Data-scripting language • Functional • MIT Licensed • Language & Runtime/Engine
4. Language + Query Engine
7. 2.0
8. Biggest Change Since 0.9
9. Clean Migration Path
10. Compatibility Layer
11. • MIT Licensed • Multi-tenanted • Telegraf, InfluxDB, Chronograf, Kapacitor rolled into 1 • OSS single server • Cloud usage based pricing • Dedicated Cloud • Enterprise on-premise
12. • MIT Licensed • Multi-tenanted • Telegraf, InfluxDB, Chronograf, Kapacitor rolled into 1 • OSS single server • Cloud usage based pricing • Dedicated Cloud • Enterprise on-premise
13. TICK is dead
14. Long Live InfluxDB 2.0 (and Telegraf)
15. Consistent Documented API Collection, Write/Query, Streaming & Batch Processing, Dashboards
17. Officially Supported Client Libraries Go, Node.js, Ruby, Python, PHP, Java, C#, C, Kotlin
18. Visualization Libraries
20. Ways to run Flux - (interpreter, InfluxDB 1.7 & 2.0)
23. Flux Language Elements
24. // get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system")
25. // get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Comments
26. // get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Named Arguments
27. // get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") String Literals
28. // get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Buckets, not DBs
29. // get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Duration Literal
30. // get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:2018-11-07T00:00:00Z) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Time Literal
31. // get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Pipe forward operator
32. // get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Anonymous Function
33. // get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => (r._measurement == "cpu" or r._measurement == “cpu") and r.host == “serverA") Predicate Function
34. // variables some_int = 23
35. // variables some_int = 23 some_float = 23.2
36. // variables some_int = 23 some_float = 23.2 some_string = “cpu"
37. // variables some_int = 23 some_float = 23.2 some_string = “cpu" some_duration = 1h
38. // variables some_int = 23 some_float = 23.2 some_string = “cpu" some_duration = 1h some_time = 2018-10-10T19:00:00
39. // variables some_int = 23 some_float = 23.2 some_string = “cpu" some_duration = 1h some_time = 2018-10-10T19:00:00 some_array = [1, 6, 20, 22]
40. // variables some_int = 23 some_float = 23.2 some_string = “cpu" some_duration = 1h some_time = 2018-10-10T19:00:00 some_array = [1, 6, 20, 22] some_object = {foo: "hello" bar: 22}
41. Data Model & Working with Tables
42. Example Series _measurement=mem,host=A,region=west,_field=free _measurement=mem,host=B,region=west,_field=free _measurement=cpu,host=A,region=west,_field=usage_system _measurement=cpu,host=A,region=west,_field=usage_user
43. Example Series _measurement=mem,host=A,region=west,_field=free _measurement=mem,host=B,region=west,_field=free _measurement=cpu,host=A,region=west,_field=usage_system _measurement=cpu,host=A,region=west,_field=usage_user Measurement
44. Example Series _measurement=mem,host=A,region=west,_field=free _measurement=mem,host=B,region=west,_field=free _measurement=cpu,host=A,region=west,_field=usage_system _measurement=cpu,host=A,region=west,_field=usage_user Field
45. Table _measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 10
46. _measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 10 Column
47. _measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 10 Record
48. _measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 10 Group Key _measurement=mem,host=A,region=west,_field=free
49. _measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 10 Every record has the same value! _measurement=mem,host=A,region=west,_field=free
50. Table Per Series _measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 11 _measurement host region _field _time _value mem B west free 2018-06-14T09:15:00 20 mem B west free 2018-06-14T09:14:50 22 _measurement host region _field _time _value cpu A west usage_user 2018-06-14T09:15:00 45 cpu A west usage_user 2018-06-14T09:14:50 49 _measurement host region _field _time _value cpu A west usage_system 2018-06-14T09:15:00 35 cpu A west usage_system 2018-06-14T09:14:50 38
51. input tables -> function -> output tables
52. input tables -> function -> output tables // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum()
53. input tables -> function -> output tables What to sum on? // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum()
54. input tables -> function -> output tables Default columns argument // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum(columns: [“_value”])
55. input tables -> function -> output tables _meas ureme host region _field _time _valu e mem A west free 2018-06- 14T09:1 10 mem A west free 2018-06- 14T09:1 11 _meas ureme host region _field _time _valu e mem B west free 2018-06- 14T09:15 20 mem B west free 2018-06- 14T09:14 22 Input in table form // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum()
56. input tables -> function -> output tables _meas ureme host region _field _time _valu e mem A west free 2018-06- 14T09:1 10 mem A west free 2018-06- 14T09:1 11 _meas ureme host region _field _time _valu e mem B west free 2018-06- 14T09:15 20 mem B west free 2018-06- 14T09:14 22 sum() // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum()
57. input tables -> function -> output tables // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum() _meas ureme host region _field _time _valu e mem A west free 2018-06- 14T09:1 10 mem A west free 2018-06- 14T09:1 11 _meas ureme host region _field _time _valu e mem B west free 2018-06- 14T09:15 20 mem B west free 2018-06- 14T09:14 22 sum() _meas ureme host region _field _time _valu e mem A west free 2018-06- 14T09:1 21 _meas ureme host region _field _time _valu e mem B west free 2018-06- 14T09:15 42
58. N to N table mapping (1 to 1 mapping)
59. N to M table mapping
60. window // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s) 30s of data (4 samples)
61. window // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s) split into 20s windows
62. window _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s) Input
63. window _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 window( every:20s) // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s)
64. window _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 window( every:20s) // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s) _meas ureme host region _field _time _valu e mem A west free …14:30 10 mem A west free …14:40 11 _meas ureme host region _field _time _valu e mem B west free …14:50 23 mem B west free …15:00 24 _meas ureme host region _field _time _valu e mem B west free …14:30 20 mem B west free …14:40 22 _meas ureme host region _field _time _valu e mem A west free …14:50 12 mem A west free …15:00 13
65. window _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 window( every:20s) // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s) _meas ureme host region _field _time _valu e mem A west free …14:30 10 mem A west free …14:40 11 _meas ureme host region _field _time _valu e mem B west free …14:50 23 mem B west free …15:00 24 _meas ureme host region _field _time _valu e mem B west free …14:30 20 mem B west free …14:40 22 _meas ureme host region _field _time _valu e mem A west free …14:50 12 mem A west free …15:00 13 N to M tables
66. Window based on time _start and _stop columns
67. group // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> group(keys:[“region"])
68. group // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> group(keys:[“region"]) new group key
69. group _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> group(keys:[“region"])
70. group _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 group( keys: [“region”]) // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> group(keys:[“region"]) _meas ureme host region _field _time _valu e mem A west free …14:30 10 mem B west free …14:30 20 mem A west free …14:40 11 mem B west free …14:40 21 mem A west free …14:50 12 mem B west free …14:50 22 mem B west free …15:00 13 mem B west free …15:00 23 N to M tables M == cardinality(group keys)
71. Group based on columns
72. Flux Design Principles
73. Useable
74. Make Everyone a Data Programmer!
78. Readable
79. Flexible
80. Composable
81. Testable
82. Contributable
83. Shareable
84. Functions Overview
85. Inputs from, fromKafka, fromFile, fromS3, fromPrometheus, fromMySQL, etc.
86. Flux != InfluxDB
91. Follow Telegraf Model
92. import "mysql" customers = mysql.from(connect: loadSecret(name:”mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results")
93. import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Imports for sharing code!
94. import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Pulling data from a non-InfluxDB source
95. import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Raw query (for now)
96. import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Loading Secret
97. import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Renaming & Shaping Data
98. import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Join on any column
99. Outputs to, toKafka, toFile, toS3, toPrometheus, toMySQL, etc.
100. Outputs are for Tasks
101. option task = { name: “Alert on disk", every: 5m, } crit = 90 // alert at this percentage warn = 80 // warn at this percentage data = from(bucket: "telegraf/autogen") |> filter(fn: (r) => r._measurement == "disk" and r._field == "used_percent") |> last() data |> filter(fn: (r) => r._value > crit) |> addColumn(key: "level", value: "critical") |> addColumn(key: "alert", value: task.name) |> to(bucket: "alerts") data |> filter(fn: (r) => r._value > warn && r._value < crit) |> addColumn(key: "level", value: "warn") |> to(bucket: "alerts")
102. option task = { name: “Alert on disk", every: 5m, } crit = 90 // alert at this percentage warn = 80 // warn at this percentage data = from(bucket: "telegraf/autogen") |> filter(fn: (r) => r._measurement == "disk" and r._field == "used_percent") |> last() data |> filter(fn: (r) => r._value > crit) |> addColumn(key: "level", value: "critical") |> addColumn(key: "alert", value: task.name) |> to(bucket: "alerts") data |> filter(fn: (r) => r._value > warn && r._value < crit) |> addColumn(key: "level", value: "warn") |> to(bucket: "alerts") Option syntax for tasks
103. option task = { name: “Alert on disk", every: 5m, } crit = 90 // alert at this percentage warn = 80 // warn at this percentage data = from(bucket: "telegraf/autogen") |> filter(fn: (r) => r._measurement == "disk" and r._field == "used_percent") |> last() data |> filter(fn: (r) => r._value > crit) |> addColumn(key: "level", value: "critical") |> addColumn(key: "alert", value: task.name) |> to(bucket: "alerts") data |> filter(fn: (r) => r._value > warn && r._value < crit) |> addColumn(key: "level", value: "warn") |> to(bucket: "alerts") Get at the last value without specifying time range
104. option task = { name: “Alert on disk", every: 5m, } crit = 90 // alert at this percentage warn = 80 // warn at this percentage data = from(bucket: "telegraf/autogen") |> filter(fn: (r) => r._measurement == "disk" and r._field == "used_percent") |> last() data |> filter(fn: (r) => r._value > crit) |> addColumn(key: "level", value: “critical") |> addColumn(key: "alert", value: task.name) |> to(bucket: "alerts") data |> filter(fn: (r) => r._value > warn && r._value < crit) |> addColumn(key: "level", value: "warn") |> to(bucket: "alerts") Adding a column to decorate the data
105. option task = { name: “Alert on disk", every: 5m, } crit = 90 // alert at this percentage warn = 80 // warn at this percentage data = from(bucket: "telegraf/autogen") |> filter(fn: (r) => r._measurement == "disk" and r._field == "used_percent") |> last() data |> filter(fn: (r) => r._value > crit) |> addColumn(key: "level", value: "critical") |> addColumn(key: "alert", value: task.name) |> to(bucket: "alerts") data |> filter(fn: (r) => r._value > warn && r._value < crit) |> addColumn(key: "level", value: "warn") |> to(bucket: "alerts") To writes to the local InfluxDB
106. Separate Alerts From Notifications!
107. option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: “slack_alert_config”), message: “_value”) |> to(bucket: “notifications")
108. option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == “critical”) // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") We have state so we don’t resend
109. option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") Use last time as argument to range
110. option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") Now function for current time
111. option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") Map function to iterate over values
112. option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") String interpolation
113. option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") Send to Slack and record in InfluxDB
114. option task = { name: "email alert digest", cron: "0 5 * * 0" } import "smtp" body = "" from(bucket: "alerts") |> range(start: -24h) |> filter(fn: (r) => (r.level == "warn" or r.level == "critical") and r._field == "message") |> group(by: ["alert"]) |> count() |> group(none: true) |> map(fn: (r) => body = body + "Alert {r.alert} triggered {r._value} times\n") smtp.to( config: loadSecret(name: "smtp_digest"), to: "alerts@influxdata.com", title: "Alert digest for {now()}", body: message)
115. option task = { name: "email alert digest", cron: "0 5 * * 0" } import "smtp" body = "" from(bucket: "alerts") |> range(start: -24h) |> filter(fn: (r) => (r.level == "warn" or r.level == "critical") and r._field == "message") |> group(by: ["alert"]) |> count() |> group(none: true) |> map(fn: (r) => body = body + "Alert {r.alert} triggered {r._value} times\n") smtp.to( config: loadSecret(name: "smtp_digest"), to: "alerts@influxdata.com", title: "Alert digest for {now()}", body: message) Cron syntax
116. option task = { name: "email alert digest", cron: "0 5 * * 0" } import "smtp" body = "" from(bucket: "alerts") |> range(start: -24h) |> filter(fn: (r) => (r.level == "warn" or r.level == "critical") and r._field == "message") |> group(by: ["alert"]) |> count() |> group(none: true) |> map(fn: (r) => body = body + "Alert {r.alert} triggered {r._value} times\n") smtp.to( config: loadSecret(name: "smtp_digest"), to: "alerts@influxdata.com", title: "Alert digest for {now()}", body: message) Closures
117. Tasks run logs (just another time series)
118. UI will hide complexity
119. Built on top of primitives
120. API for Defining Dashboards
121. Bulk Import & Export Specify bucket, range, predicate
122. Same API in OSS, Cloud, and Enterprise
123. CLI & UI
124. 2.0
125. Thank you. Paul Dix @pauldix paul@influxdata.com