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Webinar: Time-Series Data in MongoDB
 

Webinar: Time-Series Data in MongoDB

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Time series data can be found everywhere around you, from financial markets to social networks to sensors. There are a multitude of sources of time series data but they have some common attributes: ...

Time series data can be found everywhere around you, from financial markets to social networks to sensors. There are a multitude of sources of time series data but they have some common attributes: large in volume, ordered by time, and primarily aggregated for access. Time series data is a great fit for MongoDB and in this webinar we will take a closer look at how to model time series data in MongoDB by exploring the schema of a tool that has become very popular in the community: MongoDB Management Service (MMS). We'll walk through different schema design considerations and how those impact the features and functionality of MMS and review workload differences across different designs.

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  • On slide 35 you have ' Does it work with the chosen schema design?' . What is the answer to that ? And how can we use aggregation framework for this schema. Can you share some views on this.
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    Webinar: Time-Series Data in MongoDB Webinar: Time-Series Data in MongoDB Presentation Transcript

    • #mongodb Time Series Data in MongoDB Sandeep Parikh Partner Technical Services, MongoDB Inc.
    • 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 5minute resolution Display dictates the stored preaggregations, e.g. total and count
    • Monitoring Schema Design { timestamp_minute: ISODate(“2013-10-10T23:06:00.000Z”), num_samples: 58, total_samples: 108000000, type: “memory_used”, values: { 0: 999999, … 59: 1800000 } } • Per-minute document model • Documents store individual metrics and counts • Supports “total” and “avg/sec” display
    • Monitoring Data Updates 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 }} } ) • Single update required to add new data and increment associated counts
    • 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 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)” { _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)" }
    • 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-seriesdata-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