Streaming sql and druid

•

2 j'aime•852 vues

Druid provides sub-second query latency and Flink provides SQL on streams allowing rich transformation/enrichment of events as it happens. In this talk we will learn how Lyft uses flink sql and druid together to support real time analytics. Meetup: https://www.meetup.com/druidio/events/252515792/

Données & analyses

Arup Malakar | amalakar@lyft.com
1
Streaming SQL and Druid
Druid Bay Area Meetup @ Lyft

Agenda
• Use cases/motivations
• Architecture
‒ Apache Flink
‒ Druid
• Conclusion
2

Users
• Analysts
• Data Scientists
• Engineers
• Operations Team
• Executives
4
Access methods
• Dashboards
• Interactive Exploration
• Freeform SQL

Example questions
Realtime
• How is the new pickup location in SFO airport affecting the market?
Geospatial
• Are the promos we deployed earlier in a sub-region effective at moving the metrics we
thought they would move?
Anomaly
• Alert GMs when subregion conversion is low because lack of supply.
5

Limitations
• Only yesterday’s data is queryable
in analytical db
• P75 query latency in presto is 30
seconds
8
Requirements
• Data freshness < 1 minute
• P95 query latency < 5 seconds
• Geospatial support

Apache Flink - Stream processor
• Scalable/performant distributed stream processor
• API heavily influenced by Google’s Dataflow Model
• Event time processing
• APIs
‒ Functional APIs
‒ Stream SQL
‒ Direct APIs
• Joins
• Windowing
• Supports batch execution
10

Druid - Columnar database
● Scalable in-memory columnar database
● Support for geospatial data
● Extensible
● Native integration with superset
● Real time ingestion
11

Flink Stream SQL
● Familiarity with SQL
● Powerful semantics for data manipulation
● Streaming and batch mode
● Extensibility via UDF
● Joins
12

UDFs
● Geohash
● Geo region extraction
● URL cardinality reduction/normalization
○ /users/d9cca721a735d/location -> /users/{hash}/location
○ /v1//api// -> /v1/api
● User agent parsing
○ OS name / version
○ App Name / version
● Sampling
13

Validation of ingestion-spec
• Ingestion spec under source control
• Protobuf schema based compile time validation
‒ SQL
‒ Data type
‒ Column names
• Integration tests on sample data
17

Goal - all events in druid in realtime
• If you log it, you will find it
• Automagic druid spec
‒ Offline analysis for dimensions/metrics
‒ Cardinality analysis
‒ Reasonable defaults
• Auto provisioning of various resources
‒ Kafka topic for a new event
20

Conclusion
• Flink streaming SQL augments druid capabilities
‒ Transformation
‒ Joins
‒ Sampling
• Easy to use ingestion framework is crucial for adoption
22

Challenges/Next
• Good retention strategies
‒ Data size based/time based?
• Query rate limiting
• Flink batch ingestion
• Anomaly detection
• Root cause analysis
23

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1. Arup Malakar | amalakar@lyft.com 1 Streaming SQL and Druid Druid Bay Area Meetup @ Lyft

2. Agenda • Use cases/motivations • Architecture ‒ Apache Flink ‒ Druid • Conclusion 2

3. Use cases 3

4. Users • Analysts • Data Scientists • Engineers • Operations Team • Executives 4 Access methods • Dashboards • Interactive Exploration • Freeform SQL

5. Example questions Realtime • How is the new pickup location in SFO airport affecting the market? Geospatial • Are the promos we deployed earlier in a sub-region effective at moving the metrics we thought they would move? Anomaly • Alert GMs when subregion conversion is low because lack of supply. 5

6. Architecture 6

7. Earlier 7

8. Limitations • Only yesterday’s data is queryable in analytical db • P75 query latency in presto is 30 seconds 8 Requirements • Data freshness < 1 minute • P95 query latency < 5 seconds • Geospatial support

9. Now 9

10. Apache Flink - Stream processor • Scalable/performant distributed stream processor • API heavily influenced by Google’s Dataflow Model • Event time processing • APIs ‒ Functional APIs ‒ Stream SQL ‒ Direct APIs • Joins • Windowing • Supports batch execution 10

11. Druid - Columnar database ● Scalable in-memory columnar database ● Support for geospatial data ● Extensible ● Native integration with superset ● Real time ingestion 11

12. Flink Stream SQL ● Familiarity with SQL ● Powerful semantics for data manipulation ● Streaming and batch mode ● Extensibility via UDF ● Joins 12

13. UDFs ● Geohash ● Geo region extraction ● URL cardinality reduction/normalization ○ /users/d9cca721a735d/location -> /users/{hash}/location ○ /v1//api// -> /v1/api ● User agent parsing ○ OS name / version ○ App Name / version ● Sampling 13

14. Lyft Druid Spec 14

15. Flink SQL 15

16. Data Flow 16

17. Validation of ingestion-spec • Ingestion spec under source control • Protobuf schema based compile time validation ‒ SQL ‒ Data type ‒ Column names • Integration tests on sample data 17

18. Exploration 18

19. Next 19

20. Goal - all events in druid in realtime • If you log it, you will find it • Automagic druid spec ‒ Offline analysis for dimensions/metrics ‒ Cardinality analysis ‒ Reasonable defaults • Auto provisioning of various resources ‒ Kafka topic for a new event 20

21. Conclusion 21

22. Conclusion • Flink streaming SQL augments druid capabilities ‒ Transformation ‒ Joins ‒ Sampling • Easy to use ingestion framework is crucial for adoption 22

23. Challenges/Next • Good retention strategies ‒ Data size based/time based? • Query rate limiting • Flink batch ingestion • Anomaly detection • Root cause analysis 23

24. Thank you! 24

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