Managing Time Series on AWS

Cloud Conf, November 5th, 2020

Time series are a very common data format that describes how things change over time. Some of the most common sources are industrial machines and IoT devices, IT infrastructure stacks (such as hardware, software, and networking components), and applications that share their results over time. Managing time series data efficiently is not easy because the data model doesn’t fit general-purpose databases. For this reason, we built Amazon Timestream, a fast, scalable, and serverless time series database service that makes it easy to collect, store, and process trillions of time series events per day up to 1,000 times faster and at as little as to 1/10th the cost of a relational database.

Transcript

1. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
   Danilo Poccia, Chief Evangelist (EMEA)
   @danilop
   Managing Time-Series
   on AWS
   

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2. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
   Time-series data
   Time-series data is a
   sequence of data
   points recorded over a
   time interval for
   measuring events that
   change over time
   

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3. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
   What is time-series data?
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4. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
   Time-series use cases
   IoT Applications
   Collect motion or
   temperature data from the
   device sensors, interpolate
   to identify the time ranges
   without motion, or alert
   consumers to take actions
   such as turning off the
   lights to save energy
   DevOps Analysis
   Collect and analyze
   performance and health
   metrics such as
   CPU/memory utilization,
   network data, and IOPS
   to monitor health and
   optimize instance usage.
   App Analytics
   Easily store and analyze
   clickstream data at scale
   to understand the
   customer journey—the
   user activity across your
   applications over a period
   of time.
   

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5. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
   Amazon Timestream
   Fast, scalable, and serverless time-series database
   Purpose built for
   time-series data
   Built-in analytics using
   standard SQL with
   added interpolation
   and smoothing
   functions to identify
   trends, patterns, and
   anomalies
   Serverless and
   easy to use
   No servers to manage
   or instances to
   provision; software
   patches, indexes, and
   database
   optimizations are
   handled automatically
   Performance
   at scale
   Capable of ingesting trillions
   of events daily; the adaptive
   SQL query engine provides
   rapid point-in-time queries
   with its in-memory
   store, and fast analytical
   queries through its magnetic
   store
   Cost effective
   Reduces costs by
   simplifying the
   complex process of
   data lifecycle
   management; pay only
   for what you ingest,
   store and query
   Secure from
   the ground up
   All data is encrypted
   inflight, and at rest
   using AWS Key
   Management System
   (KMS) with customer
   managed keys (CMK)
   

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6. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
   Architectural concepts
   

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7. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
   2020 GA
   Present Day
   Continuous releases
   No maintenance or downtime
   serverless architecture
   

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8. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
   Terminology and concepts: Tables
   Encrypted container that holds records
   No data definition or columns are specified at creation
   Time based data retention policies for controlling data
   lifecycle within storage tiers
   

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9. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
   Terminology and concepts: Storage tiers
   Two storage tiers: in-memory and magnetic
   In-memory tier
   • Handles the ingestion of all data
   • Automatically handles data deduplication
   • Optimized for latency sensitive point-in-time queries
   Magnetic disk tier
   • Optimized for high performance analytical queries
   • Cost effective long-term storage
   

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10. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Terminology and concepts: Dimensions
    Are a set of attributes that uniquely describe a
    measurement
    Each table allows up to 128 unique dimensions
    All dimensions are represented as varchars
    Dimensions are dynamically added to the table during ingestion
    

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11. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    time region az vpc hostname measure_name measure_value::double measure_value::bigint
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 35.0 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 38.2 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 45.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 54.9 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 42.6 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 33.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 30000
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    time region az vpc hostname measure_name measure_value::double measure_value::bigint
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 35.0 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 38.2 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 45.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 54.9 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 42.6 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 33.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 30000
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    Example: Dimensions in Amazon Timestream
    

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12. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Terminology and concepts: Measures
    Each Amazon Timestream record contains a single
    measurement comprised of a name and value
    Each table supports up to 1024 unique measure names
    Measurements support: boolean, bigint, double, and varchar
    Measures are dynamically added to the table during ingestion
    

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13. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    time region az vpc hostname measure_name measure_value::double measure_value::bigint
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 35.0 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 38.2 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 45.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 54.9 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 42.6 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 33.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 30000
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    time region az vpc hostname measure_name measure_value::double measure_value::bigint
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 35.0 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 38.2 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 45.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 54.9 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 42.6 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 33.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 30000
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    time region az vpc hostname measure_name measure_value::double measure_value::bigint
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 35.0 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 38.2 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 45.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 54.9 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 42.6 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 33.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 30000
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    Example: Measures in Amazon Timestream
    

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14. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Terminology and concepts: Time-series
    Sequence of records that are represented as data points over
    a time interval for given measurement
    Every record within the series is comprised of a timestamp, at
    least one dimensions and an associated measure name/value pair
    A time-series object can be constructed by using built-in time-
    series functions
    Missing data points within a time-series object can be filled with
    interpolation functions such as last-observation-carry-forward
    

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15. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    time region az vpc hostname measure_name measure_value::double measure_value::bigint
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 35.0 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 38.2 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 45.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 54.9 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 42.6 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 33.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 30000
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    Example: Time-series in Amazon Timestream
    time region az vpc hostname measure_name measure_value::double measure_value::bigint
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 35.0 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 38.2 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju cpu_utilization 45.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 54.9 null
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 42.6 null
    2020-06-17
    19:00:02.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju mem_utilization 33.3 null
    2020-06-17
    19:00:00.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 30000
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    2020-06-17
    19:00:01.000000000
    us-east-1 1d vpc-1a2b3c4d host-24Gju networks_bytes null 15200
    

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16. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Characteristics of Amazon Timestream data
    • All records require a timestamp, one or more dimensions, a
    measurement name and measurement value
    • Records cannot be deleted or updated
    • Records are only removed when they reach the retention limit within
    the magnetic tier (indefinite storage is an option)
    • First writer wins semantics for handling duplicates
    • Multiple measures are logically represented as multiple
    individual records (one measure per record)
    • Automatically scales to handle highspeed real-time data
    ingestion
    

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17. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Data Ingestion
    

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18. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Data is written using the AWS SDK
    • Java, Python, Golang, Node.js, .NET, etc.
    • AWS CLI
    Connectivity: Data ingestion
    Adapters and plugins
    • AWS IoT Core
    • Amazon Kinesis Data Analytics for Apache Flink
    connector (GitHub)
    • Telegraf connector (GitHub)
    

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19. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Example: Amazon Timestream Ingestion (Python)
    common_attributes = {
    'Dimensions': dimensions,
    'MeasureValueType': 'DOUBLE',
    'Time': current_timestamp
    }
    cpu_utilization = {
    'MeasureName': 'cpu_utilization',
    'MeasureValue': cpu_measurement
    }
    memory_utilization = {
    'MeasureName': 'memory_utilization',
    'MeasureValue': memory_measurement
    }
    records = [cpu_utilization, memory_utilization]
    result = self.client.write_records(DatabaseName=DATABASE_NAME, TableName=TABLE_NAME,
    Records=records, CommonAttributes=common_attributes)
    

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20. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Query processing
    

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21. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Connectivity: Querying
    (Mostly) ANSI-2003 SQL for querying
    • Time-series, interpolation and gap filling functions
    • 250+ scalar, aggregate and windowing functions
    No proprietary query language to learn
    Data is queried using the AWS SDK
    • Java, Python, Golang, Node.js, .NET, etc.
    AWS CLI
    JDBC Driver
    

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22. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Example: Amazon Timestream query
    SELECT region, az, hostname, bin(time, 15s) AS binned_timestamp,
    round(avg(measure_value::double), 2) AS avg_cpu_utilization,
    round(approx_percentile(measure_value::double, 0.9), 2) AS p90_cpu_utilization,
    round(approx_percentile(measure_value::double, 0.95), 2) AS p95_cpu_utilization,
    round(approx_percentile(measure_value::double, 0.99), 2) AS p99_cpu_utilization
    FROM devops.host_metrics
    WHERE measure_name = 'cpu_utilization' -- Predicate on measure_name
    AND time > ago(2h) -- Predicate on time
    AND hostname = 'host-24Gju' -- Optional predicates on other dimensions
    GROUP BY region, hostname, az, bin(time, 15s) -- bin and GROUP BY time
    ORDER BY binned_timestamp ASC
    

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23. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Amazon Timestream integrations
    

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24. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Supported integrations and drivers
    Amazon QuickSight
    AWS IoT Core
    Grafana (Open Source Edition)
    Database Tools via JDBC
    • SQL Workbench/J, DataGrip, DBVisualizer, etc.
    AWSLabs (GitHub)
    • Kinesis Data Analytics for Apache Flink connector
    • Telegraf connector
    • Amazon SageMaker Notebook example
    

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25. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Amazon Timestream + JDBC
    

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26. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Amazon Timestream + Grafana
    

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27. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Amazon Timestream + Grafana
    

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28. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Amazon Timestream + Amazon QuickSight
    

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29. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Analytics with Timestream
    

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30. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    IoT with Timestream
    

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31. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    DevOps with Timestream
    

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32. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Demo
    

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33. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Amazon Timestream
    Server #2
    Server #3
    Server #1 cpu
    memory
    swap
    disk
    

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34. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    SELECT country, city, hostname,
    bin(time, 10m) AS binned_time,
    avg(measure_value::double) AS avg_cpu_utilization
    FROM MyDatabase.MyTable
    WHERE measure_name = 'cpu_utilization' AND time > ago(2h)
    GROUP BY country, city, hostname, bin(time, 10m)
    ORDER BY country, city, hostname, binned_time
    

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35. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    SELECT country, city, hostname,
    CREATE_TIME_SERIES(time, measure_value::double) AS cpu_utilization
    FROM MyDatabase.MyTable
    WHERE measure_name='cpu_utilization' AND time > ago(1m)
    GROUP BY country, city, hostname
    

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36. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    WITH binned_timeseries AS (
    SELECT country, city, hostname,
    bin(time, 10m) AS binned_time,
    avg(measure_value::double) AS avg_cpu_utilization
    FROM MyDatabase.MyTable
    WHERE measure_name = 'cpu_utilization' AND time > ago(2h)
    GROUP BY country, city, hostname, bin(time, 10m)
    ), interpolated_timeseries AS (
    SELECT country, city, hostname,
    INTERPOLATE_LINEAR(
    CREATE_TIME_SERIES(binned_time, avg_cpu_utilization),
    SEQUENCE(min(binned_time), max(binned_time), 1m)
    ) AS interpolated_avg_cpu_utilization
    FROM binned_timeseries
    GROUP BY country, city, hostname
    )
    SELECT *
    FROM interpolated_timeseries
    

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37. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    WITH binned_timeseries AS (
    SELECT country, city, hostname,
    bin(time, 10m) AS binned_time,
    avg(measure_value::double) AS avg_cpu_utilization
    FROM MyDatabase.MyTable
    WHERE measure_name = 'cpu_utilization' AND time > ago(2h)
    GROUP BY country, city, hostname, bin(time, 10m)
    ), interpolated_timeseries AS (
    SELECT country, city, hostname,
    INTERPOLATE_LINEAR(
    CREATE_TIME_SERIES(binned_time, avg_cpu_utilization),
    SEQUENCE(min(binned_time), max(binned_time), 1m)
    ) AS interpolated_avg_cpu_utilization
    FROM binned_timeseries
    GROUP BY country, city, hostname
    )
    SELECT country, city, hostname, time, round(avg(value), 2) AS interpolated_cpu
    FROM interpolated_timeseries
    CROSS JOIN UNNEST(interpolated_avg_cpu_utilization)
    GROUP BY country, city, hostname, time
    

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38. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Additional Resources
    

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39. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Additional Resources
    Store and Access Time Series Data at Any Scale with Amazon Timestream
    https://aws.amazon.com/blogs/aws/store-and-access-time-series-data-at-any-scale-with-amazon-timestream-now-generally-available/
    Amazon Timestream Tools and Samples
    https://github.com/awslabs/amazon-timestream-tools
    

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40. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Amazon Timestream Tools and Samples
    Sample Applications (available in various languages):
    https://github.com/awslabs/amazon-timestream-tools/tree/master/sample_apps
    Kinesis Data Analytics for Apache Flink connector example:
    https://github.com/awslabs/amazon-timestream-tools/tree/master/integrations/flink_connector
    SageMaker example:
    https://github.com/awslabs/amazon-timestream-tools/tree/master/integrations/sagemaker
    Telegraf example:
    https://github.com/awslabs/amazon-timestream-tools/tree/master/integrations/telegraf
    Continuous data-generator tools:
    https://github.com/awslabs/amazon-timestream-tools/tree/master/tools/continuous_data_ingestor
    

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41. ©, 2020 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
    Thank you!
    Please give your feedback! J
    @danilop
    

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