Time Series Data in MongoDBSenior Solutions Architect, MongoDB Inc.Massimo Brignoli

#mongodb

Agenda

• What is time series data?• Schema design considerations• Broader use case: operational intelligence• MMS Monitoring schema design• Thinking ahead• Questions

What is time series data?

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)

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

Time Series Data Considerations• Resolution of raw events• Resolution needed to support

– Applications– Analysis– Reporting

• Data retention policies– Data ages out– Retention

Schema Design Considerations

Designing For Writing and Reading• Document per event• Document per minute (average)• Document per minute (second)• Document per hour

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

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

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

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

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

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

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

MMS Monitoring Schema Design

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

MMS Monitoring

MMS Monitoring

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

Monitoring Schema Design

• Per-minute document model• Documents store 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 }}

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 }} })

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

Use Case: Operational Intelligence

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

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)”

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

Pre-Aggregated Log Data{ timestamp_minute: ISODate("2000-10-10T20:55:00Z"), resource: "/index.html", page_views: { 0: 50, … 59: 250 }}

• Leverage time-series style bucketing• Track individual metrics (ex. page views)• Improve performance for reads/writes• Minimal processing overhead

Hierarchical Aggregation

• Analytical approach as opposed to schema approach– Leverage built-in Aggregation Framework or

MapReduce• Execute multiple tasks sequentially to

aggregate at varying levels• Raw events Hourly Weekly Monthly• Rolling approach distributes the aggregation

workload

Thinking Ahead

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!

Down The Road

• Scale-out considerations– Vertical vs. horizontal (with sharding)

• Understanding the data– Aggregation– Analytics– Reporting

• Deeper data analysis– Patterns– Predictions

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

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}

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 “Hierarchical Aggregation”

• Consider secondary reads for analytical operations– Minimize load on production primaries

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

Questions?

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 Casehttp://docs.mongodb.org/ecosystem/use-cases/#operational-intelligence

• Data Modeling in MongoDBhttp://docs.mongodb.org/manual/data-modeling/

• Schema Design (webinar)http://www.mongodb.com/events/webinar/schema-design-oct2013