This presentation describes the semantics of Flink's relational APIs (SQL & Table API) on data streams. The core concept are "Dynamic Tables" which can be queried with regular SQL queries and are constantly and automatically updated.

1. FEBRUARY9, 2017, WARSAW
Stream Analytics with SQL on Apache Flink®
Fabian Hueske | Apache Flink PMC member | Co-founder dataArtisans

2. FEBRUARY9, 2017, WARSAW
Streams are Everywhere

3. FEBRUARY9, 2017, WARSAW
Data Analytics on Streaming Data
• Periodic batch processing
• Lots of duct tape and baling wire
• It’s up to you to make
everything work… reliably!
• High latency
• Continuous stream processing
• Framework takes care of failures
• Low latency

4. FEBRUARY9, 2017, WARSAW
Stream Processing in Apache Flink
• Platform for scalable stream processing
• Fast
• Low latency and high throughput
• Accurate
• Stateful streaming processing in event time
• Reliable
• Exactly-once state guarantees
• Highly available cluster setup

5. FEBRUARY9, 2017, WARSAW
Streaming Applications Powered by Flink
30 Flink applications in production for more than
one year. 10 billion events (2TB) processed daily
Complex jobs of > 30 operators running 24/7,
processing 30 billion events daily, maintaining
state of 100s of GB with exactly-once guarantees
Largest job has > 20 operators, runs on > 5000
vCores in 1000-node cluster, processes millions of
events per second

6. FEBRUARY9, 2017, WARSAW
Stream Processing is not for Everybody, … yet
• APIs of open source stream processors target developers
• Implementing streaming applications requires knowledge & skill
• Stream processing concepts (time, state, windows, triggers, ...)
• Programming experience (Java / Scala APIs)
• Stream processing technology spreads rapidly
• There is a talent gap

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What about SQL?
• SQL is the most widely used language for data analytics
• Many good reasons to use SQL
• Declarative specification
• Optimization
• Efficient execution
• “Everybody” knows SQL
• SQL would make stream processing much more accessible, but…

8. FEBRUARY9, 2017, WARSAW
No OS Stream Processor Offers Decent SQL Support
• SQL was not designed with streaming data in mind
• Relations are sets. Streams are infinite sequences.
• Records arrive over time.
• Syntax
• Time-based operations are cumbersome to specify (aggregates, joins)
• Semantics
• A SQL query should compute the same result on a batch table and a stream

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• Standard SQL and LINQ-style Table API
• Unified APIs for batch & streaming data
• Common translation layers
• Optimization based on Apache Calcite
• Type system & code-generation
• Table sources & sinks
• Streaming SQL & Table API is work in
progress
Flink’s SQL Support & Table API

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What are the Use Cases for Stream SQL?
• Continuous ETL & Data Import
• Live Dashboards & Reports
• Ad-hoc Analytics & Exploration

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Dynamic Tables
• Core concept is a “Dynamic Table”
• Dynamic tables change over time
• Dynamic tables are treated like static batch tables
• Dynamic tables are queried with standard SQL
• A query returns another dynamic table
• Stream ←→ Dynamic Table conversions without information loss
• “Stream / Table Duality”

12. FEBRUARY9, 2017, WARSAW
Stream → Dynamic Table
• Append
• Replace by Key
time k
1 A
2 B
4 A
5 C
7 B
8 A
9 B
… …
time k
2, B4, A5, C7, B8, A9, B 1, A
2, B4, A5, C7, B8, A9, B 1, A
8 A
9 B
5 C
… …

13. FEBRUARY9, 2017, WARSAW
Querying a Dynamic Table
• Dynamic tables change over time
• A[t]: Table A at time t
• Dynamic tables are queried with regular SQL
• Result of a query changes as input table changes
• q(A[t]): Evaluate query q on table A at time t
• As time t progresses, the query result is continuously updated
• similar to maintaining a materialized view
• t is current event time

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Querying a Dynamic Table
time k
k cnt
A 3
B 2
C 1
9 B
k cnt
A 3
B 3
C 1
12 C
k cnt
A 3
B 3
C 2
A[8]
A[9]
A[12]
q(A[8])
q(A[9])
q(A[12])
Table A
q:
SELECT
k,
COUNT(k) as cnt
FROM A
GROUP BY k
1 A
2 B
4 A
5 C
7 B
8 A

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time k
A[5]
A[10]
A[15]
q(A[5])
q(A[10])
q(A[15])
Table A
Querying a Dynamic Table
7 B
8 A
9 B
11 A
12 C
14 C
15 A
k cnt endT
A 2 5
B 1 5
C 1 5
q(A)
A 1 10
B 2 10
A 2 15
C 2 15
q:
SELECT
k,
COUNT(k) AS cnt,
TUMBLE_END(
time,
INTERVAL '5' SECONDS)
AS endT
FROM A
GROUP BY
k,
TUMBLE(
time,
INTERVAL '5' SECONDS)
1 A
2 B
4 A
5 C

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Can We Run Any Query on Dynamic Tables?
• No 
• There are state and computation constraints
• State may not grow infinitely as more data arrives
• Clean-up timeout must be defined
• Input updates may only trigger partial re-computation of the result
• Queries with possibly unbounded state or computation are rejected
• Optimizer performs validation

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Bounding the State of a Query
• State grows infinitely with domain of grouping attribute
• Bound query input by time
• Query aggregates data of last 24 hours. Older data is discarded.
SELECT k, COUNT(k) AS cnt
FROM A
GROUP BY k
SELECT k, COUNT(k) AS cnt
FROM A
WHERE last(time, INTERVAL ‘1’ DAY)
GROUP BY k
STOP!
UNBOUNED
STATE!

18. FEBRUARY9, 2017, WARSAW
Updating Results and Late Arriving Data
• Sometimes emitted results need to be updated
• Results which are continuously updated
• Results for which relevant records arrived late
• Results that might be updated must be kept as state
• Clean-up timeout
• When a table is converted into a stream, updates must be propagated
• Update mode
• Add/Retract mode

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Dynamic Table → Stream: Update Mode
time k
Table A
B, 1A, 2C, 1B, 2A, 3 A, 1
SELECT
k,
COUNT(k) AS cnt
FROM A
GROUP BY k
1 A
2 B
4 A
5 C
7 B
8 A
… …
Update by Key

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Dynamic Table → Stream: Add/Retract Mode
time k
Table A
+ B, 1+ A, 2+ C, 1+ B, 2+ A, 3 + A, 1- A, 1- B, 1- A, 2
1 A
2 B
4 A
5 C
7 B
8 A
… …
SELECT
k,
COUNT(k) AS cnt
FROM A
GROUP BY k
Add (+) / Retract (-)

21. FEBRUARY9, 2017, WARSAW
Current State of SQL and Table API
• Huge interest and many contributors
• Current development efforts
• Adding more window operators
• Introducing dynamic tables
• And there is a lot more to do
• New operators and features for streaming and batch
• Performance improvements
• Tooling and integration
• Try it out, give feedback, and start contributing!

22. FEBRUARY9, 2017, WARSAW
Stream Analytics with SQL on Apache Flink
Fabian Hueske | @fhueske