Mongo db 2.4 time series data - Brignoli
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Time series data is event data that is recorded and analyzed over time. This document discusses considerations for modeling time series data in MongoDB such as resolution, retention policies, and schema design. It provides examples of different schema designs including embedding data at different granularities like per document, per minute, or per hour. It also discusses use cases for time series data like operational intelligence and monitoring. Overall, the document outlines best practices for modeling, aggregating, analyzing, and scaling time series data in MongoDB.
![Storing Log Data
{
_id: ObjectId('4f442120eb03305789000000'),
host: "127.0.0.1",
user: 'frank',
time: ISODate("2000-10-10T20:55:36Z"),
path: "/apache_pb.gif",
request: "GET /apache_pb.gif HTTP/1.0",
status: 200,
response_size: 2326,
referrer: “http://www.example.com/start.html",
user_agent: "Mozilla/4.08 [en] (Win98; I ;Nav)"
}
127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326
"[http://www.example.com/start.html](http://www.example.com/start.html)" "Mozilla/4.08 [en]
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Mongo db 2.4 time series data - Brignoli
- 1. Time Series Data in MongoDB Senior Solutions Architect, MongoDB Inc. Massimo Brignoli #mongodb
- 2. Agenda • What is time series data? • Schema design considerations • Broader use case: operational intelligence • MMS Monitoring schema design • Thinking ahead • Questions
- 3. What is time series data?
- 4. Time Series Data is Everywhere • Financial markets pricing (stock ticks) • Sensors (temperature, pressure, proximity) • Industrial fleets (location, velocity, operational) • Social networks (status updates) • Mobile devices (calls, texts) • Systems (server logs, application logs)
- 5. Time Series Data at a Higher Level • Widely applicable data model • Applies to several different “data use cases” • Various schema and modeling options • Application requirements drive schema design
- 6. Time Series Data Considerations • Resolution of raw events • Resolution needed to support – Applications – Analysis – Reporting • Data retention policies – Data ages out – Retention
- 8. Designing For Writing and Reading • Document per event • Document per minute (average) • Document per minute (second) • Document per hour
- 9. Document Per Event { server: “server1”, load: 92, ts: ISODate("2013-10-16T22:07:38.000-0500") } • Relational-centric approach • Insert-driven workload • Aggregations computed at application-level
- 10. Document Per Minute (Average) { server: “server1”, load_num: 92, load_sum: 4500, ts: ISODate("2013-10-16T22:07:00.000-0500") } • Pre-aggregate to compute average per minute more easily • Update-driven workload • Resolution at the minute-level
- 11. Document Per Minute (By Second) { server: “server1”, load: { 0: 15, 1: 20, …, 58: 45, 59: 40 } ts: ISODate("2013-10-16T22:07:00.000-0500") } • Store per-second data at the minute level • Update-driven workload • Pre-allocate structure to avoid document moves
- 12. Document Per Hour (By Second) { server: “server1”, load: { 0: 15, 1: 20, …, 3598: 45, 3599: 40 } ts: ISODate("2013-10-16T22:00:00.000-0500") } • Store per-second data at the hourly level • Update-driven workload • Pre-allocate structure to avoid document moves • Updating last second requires 3599 steps
- 13. Document Per Hour (By Second) { server: “server1”, load: { 0: {0: 15, …, 59: 45}, …. 59: {0: 25, …, 59: 75} ts: ISODate("2013-10-16T22:00:00.000-0500") } • Store per-second data at the hourly level with nesting • Update-driven workload • Pre-allocate structure to avoid document moves • Updating last second requires 59+59 steps
- 14. Characterzing Write Differences • Example: data generated every second • Capturing data per minute requires: – Document per event: 60 writes – Document per minute: 1 write, 59 updates • Transition from insert driven to update driven – Individual writes are smaller – Performance and concurrency benefits
- 15. Characterizing Read Differences • Example: data generated every second • Reading data for a single hour requires: – Document per event: 3600 reads – Document per minute: 60 reads • Read performance is greatly improved – Optimal with tuned block sizes and read ahead – Fewer disk seeks
- 17. MMS Monitoring • MongoDB Management System Monitoring • Available in two flavors – Free cloud-hosted monitoring – On-premise with MongoDB Enterprise • Monitor single node, replica set, or sharded cluster deployments • Metric dashboards and custom alert triggers
- 18. MMS Monitoring
- 19. MMS Monitoring
- 20. MMS Application Requirements Resolution defines granularity of stored data Range controls the retention policy, e.g. after 24 hours only 5- minute resolution Display dictates the stored pre- aggregations, e.g. total and count
- 21. Monitoring Schema Design • Per-minute documentmodel • Documentsstore individual metrics and counts • Supports“total” and “avg/sec”display { timestamp_minute: ISODate(“2013-10-10T23:06:00.000Z”), num_samples: 58, total_samples: 108000000, type: “memory_used”, values: { 0: 999999, … 59: 1800000 } }
- 22. Monitoring Data Updates • Single update required to add new data and increment associated counts db.metrics.update( { timestamp_minute: ISODate("2013-10-10T23:06:00.000Z"), type: “memory_used” }, { {$set: {“values.59”: 2000000 }}, {$inc: {num_samples: 1, total_samples: 2000000 }} } )
- 23. Monitoring Data Management • Data stored at different granularity levels for read performance • Collections are organized into specific intervals • Retention is managed by simply dropping collections as they age out • Document structure is pre-created to maximize write performance
- 25. What is Operational Intelligence • Storing log data – Capturing application and/or server generated events • Hierarchical aggregation – Rolling approach to generate rollups – e.g. hourly > daily > weekly > monthly • Pre-aggregated reports – Processing data to generate reporting from raw events
- 26. Storing Log Data { _id: ObjectId('4f442120eb03305789000000'), host: "127.0.0.1", user: 'frank', time: ISODate("2000-10-10T20:55:36Z"), path: "/apache_pb.gif", request: "GET /apache_pb.gif HTTP/1.0", status: 200, response_size: 2326, referrer: “http://www.example.com/start.html", user_agent: "Mozilla/4.08 [en] (Win98; I ;Nav)" } 127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326 "[http://www.example.com/start.html](http://www.example.com/start.html)" "Mozilla/4.08 [en] (Win98; I ;Nav)”
- 27. Pre-Aggregation • Analytics across raw events can involve many reads • Alternative schemas can improve read and write performance • Data can be organized into more coarse buckets • Transition from insert-driven to update-driven workloads
- 28. Pre-Aggregated Log Data { timestamp_minute: ISODate("2000-10-10T20:55:00Z"), resource: "/index.html", page_views: { 0: 50, … 59: 250 } } • Leverage time-seriesstyle bucketing • Trackindividual metrics (ex. page views) • Improve performancefor reads/writes • Minimal processingoverhead
- 29. Hierarchical Aggregation • Analytical approach as opposed to schema approach – Leverage built-inAggregation Framework or MapReduce • Execute multiple tasks sequentially to aggregate at varying levels • Raw events Hourly Weekly Monthly • Rolling approach distributes the aggregation workload
- 30. Thinking Ahead
- 31. Before You Start • What are the application requirements? • Is pre-aggregation useful for your application? • What are your retention and age-out policies? • What are the gotchas? – Pre-create document structure to avoid fragmentation and performance problems – Organize your data for growth – time series data grows fast!
- 32. Down The Road • Scale-out considerations – Vertical vs. horizontal (with sharding) • Understanding the data – Aggregation – Analytics – Reporting • Deeper data analysis – Patterns – Predictions
- 33. Scaling Time Series Data in MongoDB • Vertical growth – Larger instances with more CPU and memory – Increased storage capacity • Horizontal growth – Partitioning data across many machines – Dividing and distributing the workload
- 34. Time Series Sharding Considerations • What are the application requirements? – Primarily collecting data – Primarily reporting data – Both • Map those back to – Write performance needs – Read/write query distribution – Collection organization (see MMS Monitoring) • Example: {metric name, coarse timestamp}
- 35. Aggregates, Analytics, Reporting • Aggregation Framework can be used for analysis – Does it work with the chosen schema design? – What sorts of aggregations are needed? • Reporting can be done on predictable, rolling basis – See “HierarchicalAggregation” • Consider secondary reads for analytical operations – Minimize load on production primaries
- 36. Deeper Data Analysis • Leverage MongoDB-Hadoop connector – Bi-directional support for reading/writing – Works with online and offline data (e.g. backup files) • Compute using MapReduce – Patterns – Recommendations – Etc. • Explore data – Pig – Hive
- 37. Questions?
- 38. Resources • Schema Design for Time Series Data in MongoDB http://blog.mongodb.org/post/65517193370/schema-design-for-time-series- data-in-mongodb • Operational Intelligence Use Case http://docs.mongodb.org/ecosystem/use-cases/#operational-intelligence • Data Modeling in MongoDB http://docs.mongodb.org/manual/data-modeling/ • Schema Design (webinar) http://www.mongodb.com/events/webinar/schema-design-oct2013