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Big Data + Time Series + Real-time + Analytics avec KairosDB

Big Data + Time Series + Real-time + Analytics avec KairosDB

Les time series databases sont devenues une pièce fondamentale dans le stockage et dans l’analyse de plusieurs type de données machine. A l’ère de l’Internet des Objets, ces données machines portent en elle une composante essentielle, le temps !

Alors, comment les stocker de manière efficace et scalable avec ce flot des données qui déferle tous les jours sur votre système d’information ?

Quels sont les choix, les technologies, avantages et inconvénients des différentes solutions?

Loic Coulet, ingénieur logiciel et data chez Kratos ISE, nous propose un retour d'expérience sur des technologies Big Data et Times Series. Il reviendra sur son expérience dans le déploiement d’une application pour un opérateur satellite qui restitue en temps réel des KPI et le statut de chaque service en fonction du SLA et des règles business du client. Cette application permet aussi de générer des rapports de tendances et propose des outils d’aide à l’analyse.

Vous découvrirez au cours de sa présentation KairosDB, une base de donnée time series versatile et solide qui stocke les données dans Cassandra. Loic nous montrera comment lui et son équipe ont enrichi cet outil pour répondre à des besoins spécifiques BI et analytiques de leurs clients.

http://www.meetup.com/fr/Tlse-Data-Science/events/221370114/

Toulouse Data Science

July 02, 2015
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Transcript

  1. Big Data, Time Series, Real-Time
    & Analytics with KairosDB

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  2. 2
    www.integ-europe.com | TDS – KairosDB REX |
    Agenda
    1. Presentation
    2. Storage needs
    1. Our Needs
    2. Why KairosDB & Cassandra?
    3. How does it work?
    1. Design decisions
    2. Data Model
    3. Queries & aggregations
    4. APIs
    5. Modularity

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  3. 3
    www.integ-europe.com | TDS – KairosDB REX |
    Agenda
    3. Add value to data
    1.
    Enhance existing tools
    2.
    Predict the future
    3.
    Search & Discover correlations
    4. A specific need: Satellite Business Intelligence
    1.
    Real-time BI & Time Series
    2.
    Simple configuration of a complex tool
    3.
    Reporting
    5. Conclusion
    1.
    What kairosDB can do for us
    2.
    Out contributions
    3.
    And then ?

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  4. 4
    www.integ-europe.com | TDS – KairosDB REX |
    Agenda
    Presentation

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  5. 5
    www.integ-europe.com | TDS – KairosDB REX |
    Who’s talking?
    Loic COULET
    Software
    Engineer
    Kratos ISE
    Systems
    Intregration
    Software
    Passionate
    Now Learning
    Presenting
    today
    10 years
    M&C
    CSM
    Satellite C2
    Java
    Databases
    Web
    Computer Science
    Big Data
    Analytics
    NoSQL
    Big Data
    Business Intelligence
    Learning

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  6. 6
    www.integ-europe.com | TDS – KairosDB REX |
    Kratos Integral Systems Europe (KISE)
    Subsidiary of Kratos / Kratos ISI, Labège, France
    Toulouse

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  7. 7
    www.integ-europe.com | TDS – KairosDB REX |
    Kratos Integral Systems International (KISI)
    Formerly Integral Systems Incorporated
    USA - Lanham, MD, near Washington DC

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  8. 8
    www.integ-europe.com | TDS – KairosDB REX |
    Kratos Defense & Security Solutions

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  9. 9
    www.integ-europe.com | TDS – KairosDB REX |
    Informations about KISE
    20 Employees
    System
    integrators
    …For satellite
    ground stations
    Multicutural
    company
    International
    EMEA, Asia
    Devices &
    software

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  10. 10
    www.integ-europe.com | TDS – KairosDB REX |
    KISE provides Ground stations solutions

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  11. 11
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    Storage Needs
    • Our Needs
    • Why KairosDB & Cassandra?

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  12. 12
    www.integ-europe.com | TDS – KairosDB REX |
    needs Big Data?
    A new storage…

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  13. 13
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    Big Data = 3V’s
    3 V’s

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  14. 14
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    Big Data = 3V’s
    NUMBER
    OF
    METRICS
    STORAGE
    REQUIREMENTS
    THROUGHPUT

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  15. 15
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    Data Source Systems
    CSM M&C Satellite C2 Network Mgmt

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  16. 16
    www.integ-europe.com | TDS – KairosDB REX |
    Big Data Problem?
    • Store all data for any length of time ?
    • Correlation between data sources?
    • Further analysis to detect unknown information?
    • Learning model to anticipate failures?
    AND
    THEN

    Systems
    generate… data
    amount
    Real-Time
    processing
    Legacy
    Storage
    is everything
    archived?
    How efficiently
    is data stored
    and used?

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  17. 17
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    Why KairosDB &
    Cassandra ?

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  18. 18
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    The choice drivers
    • Requirements
    • Millisecond precision
    • Efficient storage
    • Evolutive system
    • Affordable cost (R&D project, no large budget for a POC)
    • On-premises (no SaaS)

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  19. 19
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    Back in 2013 - Old generation TSDB
    In 2013 very few choices
    • Graphite http://graphite.wikidot.com/ (2006)
    • RRDtool http://oss.oetiker.ch/rrdtool/ (1999)
    • tsdb https://code.google.com/p/tsdb/ (2007)
    All based on static time buckets and no tagging.

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  20. 20
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    Back in 2013 - New Gen TSDB
    In 2013 few choices
    • OpenTSDB http://opentsdb.net/ ( 2010)
    • Druid http://druid.io/ ( end 2012)
    • Rhombus https://github.com/Pardot/Rhombus - also uses
    Cassandra (early 2013)
    • Seriesly https://github.com/dustin/seriesly (end 2012)
    • ElasticSearch stack ? http://elasticsearch.org
    • Then appeared KairosDB
    https://github.com/kairosdb/kairosdb ( 2nd quarter 2013)

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  21. 21
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    Today
    Many new choices
    • InfluxDB http://influxdb.com/ ( end 2013)
    • DalmatinerDB https://dalmatiner.io/ ( end 2013)
    • Prometheus http://prometheus.io/ (2012, open-source
    released in Jan. 2015) – Undistributed time series DB
    • SiteWhere http://www.sitewhere.org/ (early 2014)
    • Rhombus https://github.com/Pardot/Rhombus (early 2013)
    • Akulumi https://github.com/akumuli/Akumuli (end 2014)
    • yawndb http://kukuruku.co/hub/erlang/yawndb-time-series-
    database (early 2014)
    • BlueFlood http://blueflood.io/ (end 2013)
    • Newts https://github.com/OpenNMS/newts Cassandra (end
    2013)
    • SiteWhere http://www.sitewhere.org/ (early 2014)
    • … And many more

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  22. 22
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    Main Reasons
    Back in 2013: OpenTSDB Vs KairosDB on some specific requirements
    OpenTSDB KairosDB
    License GPL Apache V2
    Pluggable datastore No (WIP?) Yes
    Millisecond precision No (work-around in 2014) Yes
    Unlimited metrics & tags No (~16M) Yes
    Ad-hoc metric creation No (fixed in 2014) Yes
    Respect data integrity No Yes
    Extensible aggregation No (plugins in 2014?) Yes
    Presentation separated
    from processing
    No Yes
    Custom Data Types No Yes

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  23. 23
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    Apache Cassandra Features
    1. Distributed database (not relational)
    2. Automatic sharding & replication
    3. One node type, scale to any size
    4. Widely used, big community, strong support
    5. Free (Commercial support available)

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  24. 24
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    0
    20
    40
    60
    80
    100
    RDBMS HDF Compressed
    HDF
    KairosDB
    Overhead
    Index
    Data
    Data size per sample (in bytes)
    Relational
    Database
    Indexed File archive
    KairosDB
    ~25B
    ~50B
    ~18B ~5B ~13B
    Transactions log Efficiency matters:
    Every bit counts
    Storage solution: Data size per sample (in bytes)

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  25. 25
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    How does it work?
    • Time Series Database (KairosDB)
    • NoSQL Database as storage backend (Apache
    Cassandra)
    • Domain expertise and deep integration
    How does it
    work?
    • Design decisions
    • Data Model
    • Queries & aggregations
    • APIs
    • Modularity

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  26. 26
    www.integ-europe.com | TDS – KairosDB REX |
    How does it work?
    ONE single database with a Time Series Web Service
    frontend
    • A Time Series Database frontend (based on KairosDB)
    • A NoSQL Database as storage backend (Apache
    Cassandra) – We never query from Cassandra directly.

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  27. 27
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    The architecture
    Carrier
    Monitoring
    M&C Satellite C2 NMS
    Data Collector
    agent
    Data Collector
    agent
    Data Collector
    agent
    Data Collector
    agent
    Data Integration
    Frontend
    Reporting &
    analytics Frontend
    Storage
    Web UI
    External Analytics
    systems
    Other Data Sources

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  28. 28
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    Typical System(s)
    Frontend(s)
    Backend
    cluster
    Single Node Cluster
    • Commodity server
    • 4 to 8 TB HDD
    • 2 CPUs
    • 60GB RAM
    • Optional replica node
    Fault management Data replication
    Low cost Quick start
    Easy administration Scale to any size Best performances
    Backups

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  29. 29
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    Optimal System?
    Fault-Tolerant Small Cluster
    Fault management Data replication
    Low cost Quick start
    Easy administration Scale to any size Best performances
    Backups
    Replication
    Factor: x3

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  30. 30
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    Design
    Decisions

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  31. 31
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    Design For Efficiency
    • Data is stored as time series

    Data field name / metric name

    Value

    Timestamp

    Contextual information (tag key/value pairs)
    • Optimize for large queries
    • Leverage Cassandra design

    Row key = aggregate of metric + base timestamp + tag keys/values

    Use column qualifier as timestamp offset, leverage wide rows
    Row key 0 . . . N . . . X Y Z
    X001{12234400}tag1=val1;tag2=val2… val1 valN ValY
    X001{12234400}tag1=val3;tag2=val2… ValX
    X002{12234400}tag1=val1;tag2=val2… val1 ValZ

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  32. 32
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    Design decisions
    The design decisions were oriented towards
    • Simplicity
    • Scalability (one to N nodes)
    • Storage efficiency– a few bytes per sample (eq. DM4)
    • Fast processing (per node: 100K/s in - 500K/s out)
    • Flexibility and evolution
    • A unified format using time series

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  33. 33
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    Data model
    Model is the same for all : time series
    How are the data points organized in the database?
    Datapoint
    - Metric
    - Timestamp
    - Value
    - Tags
    • Metric: identifies the measurement
    e.g. a telemetry mnemonic in Raw, Eu or State
    conversion
    • Timestamp: time of the measurement (ms)
    • Value: measurement itself, values are typed
    • Tags: characteristics of the measurement e. g.
    satellite or stream. Or quality of the
    measurement.

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  34. 34
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    Data types
    Values are typed. A value can be of any data type.
    KairosDB provides basic types:
    • String / numerical (float, long) / complex
    Other data types are possible, including composite types :
    • Just implement an interface and a factory

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  35. 35
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    System, queries & Ad-hoc
    aggregations

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  36. 36
    www.integ-europe.com | TDS – KairosDB REX |
    Presentation of the system architecture
    How do the systems interact?
    server
    Backend
    cluster
    Work
    station
    frontend query
    backend query
    raw data processing raw data

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  37. 37
    www.integ-europe.com | TDS – KairosDB REX |
    Presentation of the system architecture
    The two roles of the server
    Frontend
    query
    server
    Processing
    instructions
    Data
    selection
    - Data selection instruction are forwarded to the cluster to get raw data
    - Processing instructions are kept by the server to process the raw data

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  38. 38
    www.integ-europe.com | TDS – KairosDB REX |
    System throughput
    First implication: transfer speed
    server
    Backend
    cluster
    Work
    station
    SLOW
    FAST

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  39. 39
    www.integ-europe.com | TDS – KairosDB REX |
    System throughput
    First implication: transfer speed
    You are interested in the evolution of the central frequency of one carrier
    - 3,600 datapoints an hour
    - 86,400 datapoints a day
    - 31,536,000 datapoints a year
    SLOW
    FAST
    31,536,000 DP 31,536,000 DP

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  40. 40
    www.integ-europe.com | TDS – KairosDB REX |
    System throughput
    What is the solution?
    You do not need 31,536,000 datapoints to see the evolution on one year
     Use aggregation
    SLOW
    FAST
    31,536,000 DP 365 DP
    Daily
    average

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  41. 41
    www.integ-europe.com | TDS – KairosDB REX |
    System throughput
    Second implication: data volume
    server
    Backend
    cluster
    Work
    station
    Very large data
     10s of terabytes
    Large but limited
    memory
     10s of gigabytes
    Very limited memory
     Few gigabytes

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  42. 42
    www.integ-europe.com | TDS – KairosDB REX |
    Data model
    How do you query datapoints?
    • Metric name (mandatory)
    o
    E.g. metric.name
    • The tag filtering (optional)
    o
    E.g. source=src01, quality=good
    This will return all the datapoints that match these criteria
     This might return too many points
    Day 1 Day 2 Day 3
    Points from antenna 1 or 2

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  43. 43
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    Query model
    How do you query datapoints?
    Group by Filtering Aggregation
    Data reduction steps

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  44. 44
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    Query model
    And Then?
    Group by Filtering Aggregation
    Data reduction steps
    V. Aggregation
    Prediction
    Serialization
    RESULTS

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  45. 45
    www.integ-europe.com | TDS – KairosDB REX |
    1. All features are provided as web services (HTTP / REST)
    2. Open APIs
    3. Interoperable data format based on JSON
    4. Intuitive Web UI for starting using the system
    5. APIs include:
    • Data acquisition
    • Data querying
    • Analysis features (prediction, correlations)
    Interoperability Features

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  46. 46
    www.integ-europe.com | TDS – KairosDB REX |
    Query API
    • KairosDB provide Web services for performing queries
    • Queries are JSON documents
    {
    "start_absolute": 1431986400000,
    "metrics": [
    {
    "name": "kairosdb.jvm.free_memory",
    "limit": 1000000,
    "group_by": [
    {
    "name": "tag",
    "tags": ["host"]}],
    "aggregators": [
    {
    "name": "avg",
    "sampling": {
    "value": 1,
    "unit": "hours"},
    "align_start_time": true,
    "align_sampling": true}]}],
    "cache_time": 0
    }

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  47. 47
    www.integ-europe.com | TDS – KairosDB REX |
    Query Engine & aggregations
    • Ad-hoc queries and statistics calculation
    • Business Intelligence features already implemented (aggregate, drill & pivot)
    • Data aggregates: Min, Max, Sum, Average, Count, Rate, Std Deviation…etc
    • Multi-level Group-by feature using tags, value, or time
    • Filter by tags values

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  48. 48
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    Aggregations
    Usually a function used to reduce or summarize the
    number of samples (features)
    In kairosDB an aggregator can do almost anything
    Group by Filtering Aggregation
    Data reduction steps

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  49. 49
    www.integ-europe.com | TDS – KairosDB REX |
    Aggregators are designed to be chained
    Work in streaming: Fast and memory-efficient
    Aggregations
    5min Avg Derivative Derivative 1day Sum

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  50. 50
    www.integ-europe.com | TDS – KairosDB REX |
    • Min
    • Max
    • Avg
    • Sum
    • Std Dev
    • Scale
    • Rate (Derivative)
    • Least Square
    • Count
    • Percentile
    Aggregations: Available aggregators
    KairosDB

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  51. 51
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    Flexibility & Modularity

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  52. 52
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    KairosDB is modular
    core
    Module
    A
    Module
    B
    Module
    C
    Modules may add new:
    - Features, services
    - Web service API
    endpoints
    - Data types
    - Aggregators
    - Predictors
    - Query processor
    - New correlation models
    - Datastore(s)
    - …

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  53. 53
    www.integ-europe.com | TDS – KairosDB REX |
    Datastore Module
    Pluggable DataStore
    core
    HDF 5
    kairosdb.service.datastore=org.kairosdb.datastore.cassandra.CassandraModule

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  54. 54
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    Our usage: examples
    core
    Real-time
    Monitoring
    Dashboard
    Module
    Custom
    Analytics
    Module
    External
    Systems
    Dashboard
    Reporting Analytics

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  55. 55
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    • Enhance existing tools
    • Predict the future
    • Search & Discover correlations
    Add Value to the
    data

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  56. 56
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    Enhance The existing

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  57. 57
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    Need for better aggregations
    We doubled the amount of aggregators

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  58. 58
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    Issues
    • KairosDB time-windowed aggregation model is only
    “horizontal”

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  59. 59
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    Issues
    • So it is highly affected by the series sampling rate

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  60. 60
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    Issues
    • We also needed vertical aggregations – and made it

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  61. 61
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    • Min
    • Max
    • Avg
    • Sum
    • Diff
    • Preference
    Aggregations: our vertical aggregators

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  62. 62
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    Trying to predict the future

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  63. 63
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    1. Generic predictive analysis (in the query engine) is being implemented
    2. Several predictors : linear (exponential Smoothing, holt, least squares), or
    dymanic with Dynamic Linear Model (DLM)
    Time Series Prediction analysis
    Actual Data Prediction

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  64. 64
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    Search & Discover correlations

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  65. 65
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    Correlate one reference series to many others
    Search correlations
    Interactive histogram represents most correlated series
    eirp carrier=Carrier_1_Ref

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  66. 66
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    Same query model than in correlations search
    Time Series Correlations Discovery
    Interactive correlation
    matrix

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  67. 67
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    • Real-time BI & Time Series
    • Simple configuration of a complex tool
    • Reporting
    A specific need: Satellite
    Business Intelligence

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  68. 68
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    Real-time Business Intelligence
    Specific requirements
    have been provided by
    Es’hailsat.
    Determination of the
    service status from
    special business rules.
    Configurable through CSV
    file
    Reconfiguration of
    Compass devices from
    the dashboard
    Business
    Evaluation
    Analyze
    Implement
    KPI
    Evaluate

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  69. 69
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    Satellite Business Intelligence = ?
    Business…

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  70. 70
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    Business
    Evaluation
    Analyze
    Implement
    KPI
    Evaluate
    Satellite Business Intelligence = ?
    + Intelligence…

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  71. 71
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    KPI?

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  72. 72
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    …For your satellite
    services

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  73. 73
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    Correlations for services monitoring dashboard
    Metrics KPI
    Limit
    checking
    Rules
    SLA
    Check
    Rules
    Services
    Report
    Data
    Source
    System
    Correlations
    Data
    Source
    System
    + Business rules

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  74. 74
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    THE Dashboard
    Es’hailsat Monitoring Dashboard displays on a Web Browser with real-time information

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  75. 75
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    Configuration using a CSV file
    The CMC Monitoring Dashboard configuration file (CSV file) is edited manually by the CMC operator.
    Configures:
    • KPI Thresholds
    • Monitoring Plans
    • Monitored Services

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  76. 76
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    10- Conclusion
    • What kairosDB can do for us
    • Out contributions
    • And then ?

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  77. 77
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    What KairosDB can
    do for us

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  78. 78
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    KairosDB Features
    • System operational and robust
    • On the fly statistical generation
    • Batch generation (rollups) expected soon – planned on KairosDB
    • System is:
    • Fast
    • Scalable (1 to N nodes)
    • Fault tolerant (1 to N replicas)
    • Easy to backup (e.g. Cassandra snapshots files)
    • Modular and evolutive

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  79. 79
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    Lead to a simple system
    Thanks to KairosDB our system is simple, robust and
    versatile
    • Thanks to this system we could build efficient solution for
    generic and bespoke features

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    Contributions

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    Existing integrated toolsuite
    Because its model is simple with common web services
    API we could easily integrate it with
    • A reporting tool (using BIRT)
    • A real-time dashboard (using Grafana)
    • A Scientific computing environment (R)

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    Using BIRT reporting tool
    Reporting With BIRT

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    Grafana Real-time Dashboard – KairosDB
    Plugin

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    R interface

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    Library interfacing with R statistical environment
    R interface
    # Load the library
    library("kairosdb")
    # Create a metric queries
    metric1 = KairosMetric('kairosdb.jvm.free_memory',aggregators =
    aggregator.avg(1,TimeUnit.HOURS,alignSampling = TRUE), tagGroupBy = "host")
    metric2 = KairosMetric('kairosdb.jvm.max_memory',aggregators =
    aggregator.avg(1,TimeUnit.HOURS,alignSampling = TRUE), tagGroupBy = "host")
    # Query & prepare results
    query = KairosMetricQuery(list(metric1,metric2),'05/19/2015')
    response = executeQuery(query,'http://localhost:8081/api/v1/datapoints/query')
    series = getSeriesByTag(response, 'host')
    timestampsAsDate = convertTimestampsToDate(series[,'timestamp'])
    # plot results
    plot(timestampsAsDate,series[,'value'])

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    • KairosDB will implement rollups (automatic pre-aggregation of
    data)
    • We keep on moving on time series
    Keep moving

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  88. Thank You !

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  89. Any Questions?

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