Making sense of your data
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The document discusses building a custom data source for Grafana using Vert.x to retrieve time series data stored in MongoDB. It describes how to connect Grafana and MongoDB through a three-tier architecture using a Vert.x microservice. The microservice would handle splitting requests, querying the database in parallel, aggregating results, and performing additional processing before returning data to Grafana. Vert.x is well-suited for this due to its asynchronous, reactive, and scalable nature. Sample code is available on GitHub to demonstrate the approach.
![/search Format
Request
{
"target" : "select metric",
"refId" : "E"
}
Response
[
"Metric Name 1",
"Metric Name2",
]
An array of strings
13](https://image.slidesharecdn.com/makingsenseofyourdata-170302144639/85/Making-sense-of-your-data-12-320.jpg)
![/annotations Format
Request
{ "annotation" : {
"name" : "Test",
"iconColor" : "rgba(255, 96, 96, 1)",
"datasource" : "Simple Example DS",
"enable" : true,
"query" : "{"name":"Timeseries A"}" },
"range" : {
"from" : "2016-06-13T12:23:47.387Z",
"to" : "2016-06-13T12:24:19.217Z" },
"rangeRaw" : {
"from" : "2016-06-13T12:23:47.387Z",
"to" : "2016-06-13T12:24:19.217Z"
} }
Response
[ { "annotation": {
"name": "Test",
"iconColor": "rgba(255, 96, 96, 1)",
"datasource": "Simple Example DS",
"enable": true,
"query": "{"name":"Timeseries A"}" },
"time": 1465820629774,
"title": "Marker",
"tags": [
"Tag 1",
"Tag 2" ] } ]
14](https://image.slidesharecdn.com/makingsenseofyourdata-170302144639/85/Making-sense-of-your-data-13-320.jpg)
![/query Format
Request
{ "panelId" : 1,
"maxDataPoints" : 1904,
"format" : "json",
"range" : {
"from" : "2016-06-13T12:23:47.387Z",
"to" : "2016-06-13T12:24:19.217Z" },
"rangeRaw" : {
"from" : "2016-06-13T12:23:47.387Z",
"to" : "2016-06-13T12:24:19.217Z" },
"interval" : "20ms",
"targets" : [ {
"target" : "Time series A",
"refId" : "A" },] }
Response
[ { "target":"Timeseries A",
"datapoints":[
[1936,1465820629774],
[2105,1465820632673],
[4187,1465820635570],
[30001,1465820645243] },
{ "target":"Timeseries B",
"datapoints":[ ] }
]
15](https://image.slidesharecdn.com/makingsenseofyourdata-170302144639/85/Making-sense-of-your-data-14-320.jpg)
Making sense of your data
- 1. Making Sense of your Data Building A Custom DataSource for Grafana with Vert.x Gerald Mücke DevCon5 GmbH @gmuecke
- 2. About me IT Consultant & Java Specialist at DevCon5 (CH) Focal Areas Tool-assisted quality assurance Performance (-testing, -analysis, -tooling) Operational Topics (APM, Monitoring) Twitter: @gmuecke 3
- 3. The Starting Point Customer stored and keep response time measurement of test runs in a MongoDB Lots of Data Timestamp & Value No Proper Visualization 4
- 4. What are timeseries data? a set of datapoints with a timestamp and a value time value
- 5. What is MongoDB? MongoDB NoSQL database with focus on scale JSON as data representation No HTTP endpoint (TCP based Wire Protocol) Aggregation framework for complex queries 6
- 6. What is Grafana? A Service for Visualizing Time Series Data Open Source Backend written in Go Frontend based on Angular Dashboards & Alerts
- 7. Grafana Architecture 8 Grafana Server • Implemented in GO • Persistence for Settings and Dashboards • Offers Proxy for Service Calls Browser Angular UI Data Source Data Source Plugin... Proxy DB DB
- 8. Datasources for Grafana 9 Grafana Server • Implemented in GO • Persistence for Settings and Dashboards • Offers Proxy for Service Calls Browser Datasource Angular UI Data Source Plugin • Angular • JavaScript HTTP
- 9. Connect Angular Directly to Mongo? 10
- 10. From 2 Tier to 3 Tier 11 Grafana (Angular) Mongo DB Grafana (Angular) Mongo DB Datasource Service HTTP Mongo Wire Protocol
- 11. Start Simple SimpleJsonDatasource (Plugin) 3 ServiceEndpoints /search Labels – names of available timeseries /annoations Annotations – textual markers /query Query – actual time series data 12 https://github.com/grafana/simple-json-datasource
- 12. /search Format Request { "target" : "select metric", "refId" : "E" } Response [ "Metric Name 1", "Metric Name2", ] An array of strings 13
- 13. /annotations Format Request { "annotation" : { "name" : "Test", "iconColor" : "rgba(255, 96, 96, 1)", "datasource" : "Simple Example DS", "enable" : true, "query" : "{"name":"Timeseries A"}" }, "range" : { "from" : "2016-06-13T12:23:47.387Z", "to" : "2016-06-13T12:24:19.217Z" }, "rangeRaw" : { "from" : "2016-06-13T12:23:47.387Z", "to" : "2016-06-13T12:24:19.217Z" } } Response [ { "annotation": { "name": "Test", "iconColor": "rgba(255, 96, 96, 1)", "datasource": "Simple Example DS", "enable": true, "query": "{"name":"Timeseries A"}" }, "time": 1465820629774, "title": "Marker", "tags": [ "Tag 1", "Tag 2" ] } ] 14
- 14. /query Format Request { "panelId" : 1, "maxDataPoints" : 1904, "format" : "json", "range" : { "from" : "2016-06-13T12:23:47.387Z", "to" : "2016-06-13T12:24:19.217Z" }, "rangeRaw" : { "from" : "2016-06-13T12:23:47.387Z", "to" : "2016-06-13T12:24:19.217Z" }, "interval" : "20ms", "targets" : [ { "target" : "Time series A", "refId" : "A" },] } Response [ { "target":"Timeseries A", "datapoints":[ [1936,1465820629774], [2105,1465820632673], [4187,1465820635570], [30001,1465820645243] }, { "target":"Timeseries B", "datapoints":[ ] } ] 15
- 15. Structure of the Source Data { "_id" : ObjectId("56375bc54f3c4caedfe68aca"), "t" : { "eDesc" : "Some more verbose description of the datapoint than the name", "eId" : "56375ae24f3c4caedfe68a07", "name" : "a name of the datapoint series", "profile" : "P01", "rnId" : "56375b694f3c4caedfe68aa0", "rnStatus" : "OK", "uId" : "anonymous" }, "n" : { "begin" : NumberLong("1446468494689"), "value" : NumberLong(283) } } 16
- 16. Custom Datasource Should be Lightweight Fast / Performant Simple 17
- 17. Microservice? Options for implementation Java EE Microservice (i.e. Wildfly Swarm) Springboot Microservice Vert.x Microservice Node.js ... 19
- 18. The Alternative Options Node.js Single Threaded Child Worker Processes Javascript Only Not best-choice for heavy computation Spring / Java EE Multithreaded Clusterable Java Only Solid Workhorses, cumbersome at times 21
- 19. Why Vert.x? High Performance, Low Footprint Asynchronous, Non-Blocking Actor-like Concurrency Event & Worker Verticles Message Driven Polyglott Java, Groovy, Javascript, Scala … Scalable Distributed Eventbus Multi-threaded Event Loops 22
- 20. Asynchronous non-blocking vs Synchronous blocking 23
- 21. Event Loop 24 Photo: Andreas Praefcke
- 22. Event Loop and Verticles 25 Photo: RokerHRO 3rd Floor, Verticle A 2nd Floor, Verticle B 1st Floor, Verticle C
- 23. 26
- 24. 27
- 27. CPU Multi-Reactor 31 Core Core Core Core Eventbus Other Vert.x Instance Browser Verticle Verticle
- 28. Event & Worker Verticles Event Driven Verticles Worker Verticles 32 Verticle Verticle Verticle Thread Pool Thread Pool Verticle Verticle Verticle Verticle Verticle
- 29. Implementing the datasource Http Routes Pre-Processing Optional, for example: Split Request Query Database for Time Data Points Post Processing Optional, for example: Merge, Calculations 33
- 30. Step 1 – The naive approach Find all datapoints within range 34
- 31. Step 2 – Split Request Split request into chunks (#chunks = #cores) Use multiple Verticle Instance in parallel (#instances = #cores) ? 35 CPU
- 32. Step 4 – Aggregate Datapoints Use Mongo Aggregation Pipeline Reduce Datapoints returned to service 37
- 33. Step 5 – Percentiles (Service) Fetch all data Calculate percentiles in service 38 CPU
- 34. Step 6 – Percentiles (DB) Build aggregation pipeline to calculate percentiles Algorithm, see http://www.dummies.com/education/math/statistics/how-to- calculate-percentiles-in-statistics/ 39 DB
- 36. Remember the CELL BE? CPU of the PS3 Central Bus (EIB) 1 General Purpose CPU Runs the Game (Event) Loop 8 SPUs Special Processing Sound Physics AI ... 41
- 37. Adding more stats & calculation Push Calculation to DB if possible Add more workers / node for complex (post-) processing Aggregate results before post-processing DB performance is king 42
- 38. Takeaways Grafana is powerful tool for visualizing data Information becomes consumable through visualization Information is essential for decision making Vert.x is Reactive, Non-Blocking, Asynchronous, Scalable Running on JVM Polyglott Fun 47 Source code on: https://github.com/gmuecke/grafana-vertx-datasource
- 39. Gerald Mücke www.devcon5.ch DevCon5 @gmuecke