Streaming analytics state of the art

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Streaming Analytics: State of The Art
Stavros Kontopoulos
Senior Software Engineer @ Lightbend, M.Sc.

@s_kontopoulos
Who am I?
2
skonto
s_kontopoulos
S. Software Engineer @ Lightbend, Fast Data Team
Apache Flink
Contributor at
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Agenda
- Streaming Analytics - What & Why & How
- Streaming Platforms - Streaming Engines
- Code examples & Demo
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Insights
Data Insight: A conclusion, a piece of information which can be used to make
actions and optimize a decision-making process.
Customer Insight: A non-obvious understanding about your customers, which if
acted upon, has the potential to change their behaviour for mutual benefit
Customer insight, Wikipedia
DAT
A
INFO INSIGHTS ACTIONS
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The Gap
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DATA INSIGHTS

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Streaming Analytics - Bridging the Gap
Collect Analyze
Data Output Flow
(alarms, visualizations, ML scoring, etc)Data Input Flow
(sensors, mobile apps, etc)
Permanent Store
DATA FLOW
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Streaming Analytics
“Streaming Analytics is the acquisition and analysis of the data at the moment it
streams into the system. It is a process done in a near real-time(NRT) fashion and
analysis results trigger specific actions for the system to execute.“
● No constraints or deadlines in the way they exist in RT systems
● Processing delay (end-to-end) varies and depends on the application ( < 1 ms
to minutes)
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Big Data vs Fast Data
● Data in motion is the key characteristic.
● Fast Data is the new Big Data!
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Two categories of systems: batch vs
streaming systems.

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Common Use Cases
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Image: Lightbend Inc.

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Speed?
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Batch Data Pipeline
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Analysis
New Data Batch View
Traditional MapReduce
paradigm

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Streaming Data Pipeline
In memory processing as data flows...
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Analysis
New Data NR-Time View
Apache Flink
Akka Streams
Streaming Platform
Apache Kafka
Streams

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Streaming Platforms
Its an ecosystem/environment that supports building and running streaming
applications. At its core it uses a streaming engine. Example of tools:
● A durable pub/sub component to fetch or store data
● A streaming engine
● A registry for storing data metadata like the data format etc.
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Streaming Platforms - Some Examples
- Fast Data Platform (https://www.lightbend.com/products/fast-data-platform)
- Confluent Enterprise (https://www.confluent.io/product/confluent-platform)
- Da-Platform-2 (https://data-artisans.com/da-platform-2)
- Databricks Platform (https://databricks.com/product/unified-analytics-platform)
- IBM Streams
(https://www.ibm.com/analytics/us/en/technology/stream-computing/
- MapR Streams (https://mapr.com/products/mapr-streams/)
- Pravega (http://pravega.io)
...
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Streaming Engine - the Core
A streaming engine provides the basic capabilities for developing and deploying
streaming applications.
Some systems like Kafka Streams or Akka Streams which
are just libraries don’t cover deployment effectively.
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Streaming Engine - Key Features I
● Fault - Tolerance
● Processing Guarantees
● Checkpointing
● Streaming SQL
● Batch - Streaming API
● Language Integration (Python, Java, Scala, R)
● Stateful Management, User Session state
● Locality Awareness
● Backpressure
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Streaming Engine - Key Features II
● Multi-Scheduler Support: Yarn, Mesos, Kubernetes
● Micro batching vs Data Flow
● ML, Graph, CEP
● Connectors (Sources, Sinks)
● Memory - Disk management (shuffling)
● Security (Kerberos etc)
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DataFlow Execution Model
User defines computations/operations (map, flatMap etc) on the data-sets
(bounded or not) as a DAG. The data-sets are considered as immutable
distributed data. DAG is shipped to nodes where the data lie, computation is
executed and results are sent back to the user.
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Spark Model
example
Flink model - FLIP 6

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Streaming Engine - Which one to choose?
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Some engines to consider...

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The Modern Enterprise Fast Data Architecture
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Infrastructure (on premise, cloud)
Cluster scheduler
(Yarn, Standalone, Kubernetes, Mesos)
Fast Data
Apps
Micro
Services
ML
Operations
Monitoring
Security
Governance
Permanent
Storage
(HDFS, S3...)
Streaming Platform
(pub/sub, streaming engine, etc)
BI Data Lake

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Example Fast Data Architecture for the Enterprise
21Image: Lightbend Inc.

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Analyzing Data Streams
Processing infinite data streams imposes certain restrictions compared to batch
processing:
- We may need to trade-off accuracy with space and time costs eg use
approximate algorithms or sketches eg count-min for summarizing stream
data.
- Streaming jobs require to operate 24/7 and need to be able to adapt to code
changes, failures and load variance.
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● Data flows from one or more sources through the engine and is written to one
or more sinks.
● Two cases for processing:
○ Single event processing: event transformation, trigger an alarm on an error event
○ Event aggregations: summary statistics, group-by, join and similar queries. For example
compute the average temperature for the last 5 minutes from a sensor data stream.
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● Event aggregation introduces the concept of windowing wrt to the notion of
time selected:
○ Event time (the time that events happen): Important for most use cases where context and
correctness matter at the same time. Example: billing applications, anomaly detection.
○ Processing time (the time they are observed during processing): Use cases where I only care
about what I process in a window. Example: accumulated clicks on a page per second.
○ System Arrival or Ingestion time (the time that events arrived at the streaming system).
● Ideally event time = processing time. Reality is: there is skew.
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● Windows come in different flavors:
○ Tumbling windows discretize a stream into non-overlapping windows.
○ Sliding Windows: slide over the stream of data.
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Images:https://flink.apache.org/news/2015/12/0
4/Introducing-windows.html

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● Watermarks: indicates that no elements with a timestamp older or equal to
the watermark timestamp should arrive for the specific window of data. Marks
the progress of the event time.
● Triggers: decide when the window is evaluated or purged. Affect latency &
state kept.
● Late data: provide a threshold for how late data can be compared to current
watermark value.
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● Recent advances (like the concept of watermarks etc) in Streaming are a
result of the pioneer work:
○ MillWheel: Fault-Tolerant Stream Processing at Internet Scale, VLDB
2013.
○ The Dataflow Model: A Practical Approach to Balancing Correctness,
Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data
Processing, Proceedings of the VLDB Endowment, vol. 8 (2015), pp.
1792-1803
● The-world-beyond-batch-streaming-101 (Tyler Akidau)
● The-world-beyond-batch-streaming-102 (Tyler Akidau)
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● Apache Beam is the open source successor of Google’s
DataFlow.
● Provides the advanced semantics needed for the current
needs in streaming applications.
● Google DataFlow, Apache Flink, Apache Spark follow
that model.
(https://beam.apache.org/documentation/runners/capabili
ty-matrix)
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Streams meet distributed log - I
Streams fit naturally to the idea of the distributed log (eg. Kafka streams integrated
with Kafka or Dell/EMC’s Pravega* uses stream as a storage primitive on top of
Apache Bookeeper).
*Pravega is an open-source streaming storage system.
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Streams meet distributed log - II
Distributed log possible use cases:
● Implement external services (micro-services)
● Implement internal operations (eg. kafka streams shuffling, fault-tolerance)
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Processing Guarantees
Many things can go wrong…
● At-most once
● At-least once
● Exactly once
What are the boundaries?
Within the streaming engine?
How about end-to-end including sources and sinks?
How about side effects like calling an external service?
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Table Stream Duality
Stream table : The aggregation of a stream of updates over time yields a
table.
Table stream: The observation of changes to a table over time yields a
stream.
Why is this useful?
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Streaming SQL Queries
Semantics ? How we define a join on an unbounded stream? Table join?
There is a joint work from:
https://docs.google.com/document/d/1wrla8mF_mmq-NW9sdJHYVgMyZsgCmHu
mJJ5f5WUzTiM/
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Apache Flink

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Streaming Applications - Spark Structured Streaming API
create spark session and read
from kafka topic

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Streaming Applications - Spark Structured Streaming API
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sensor metadata
emit complete output for every
window update based on
event-time to console. Setup a
trigger.

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Streaming Applications - Flink Streaming API
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custom source
initial sensor values

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watermark generation
create some random data

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create a windowed keyed
stream
apply a function per window

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Kafka Streams vs Beam Model
- Trigger is more of an operational aspect compared to business parameters
like the window length. How often do I update my computation (affecting
latency and state size) is a non-functional requirement.
- A Table covers both the case of immutable data and the case of updatable
data.
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Kafka Streams vs Beam Model
KTable<Windowed<String>, Long> aggregated = inputStream
.groupByKey()
.reduce((aggValue, newValue) -> aggValue + newValue,
TimeWindows.of(TimeUnit.MINUTES.toMillis(2))
.until(TimeUnit.DAYS.toMillis(1) /* keep for one day */),
"queryStoreName");
props.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 100 /* milliseconds */);
props.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 10 * 1024 * 1024L);
https://www.confluent.io/blog/watermarks-tables-event-time-dataflow-model
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Source code: http://bit.ly/2yhDCeN

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Thank you! Questions?
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https://github.com/skonto/talks/blob/master/big-data-italy-2017/streaming
-analytics/references.md

Streaming analytics state of the art

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