Conquering the Lambda architecture in LinkedIn metrics platform with Apache Calcite and Apache Samza

Conquering the Lambda architecture in LinkedIn metrics platform with Apache Calcite and Apache Samza Khai Tran Staff Software Engineer

Agenda ● Overview of LinkedIn metrics platform ● Moving from offline to nearline ● Under the hood of the nearline architecture ● Nearline production usecase ● Conclusion

Overview of LinkedIn metrics platform

Metrics @ LinkedIn ● Metrics = Measurements over tracking data ● Crucial for decision making: ○ Experimentation - test everything ○ Reporting - monitor and alert ○ In production, site-facing applications

We provide: ● A trusted repository of metrics ● A self-serve platform for sustainable lifecycle of metrics In production Experimentation Reporting Primary Data Unified Metrics Platform LinkedIn unified metrics platform (UMP)

Growth of UMP Metrics 2016 20172015 6800 4680 1100 Current: 8000+ metrics

# code LOAD … # data # transformation # code STORE … # config Metrics: - A = SUM(A’) - B = Unique(id) Downstream: - XLNT - Raptor UMP User Code Platform Generated Code To App To App DefineDeclare Onboard Data Metadata Onboarding process User

Moving from offline to nearline

Offline computation flows Hourly job latency: 3-6 hours -> want realtime/nearline ...... Metric union User code User code Cubing, Rollup Dimension augmentation HDFS tables Dali views Pinot, Presto Azkaban execution Espresso, Oracle, MySQL

... What we want for nearline flows Metric unionUser code User code Samza job Dimension augmentation Pinot

Latency is not the only requirement Easy to onboard ● Minimum effort to convert existing offline into nearline ● Easy to write user code for new nearline flows Easy to maintain ● Just one version of user code - single source of truth ● Run as a service Latency ● ~5 - 30 mins

Samza jobs Putting things together Pinot Batch jobs UMP realtime platform UMP offline platform HDFS Raptor Lambda architecture with a single codebase code configMetrics definition

Current support User code in Pig ● LOAD, STORE ● FILTER, SAMPLE, SPLIT, UNION ● Simple FOREACH ● JOIN - all semantics ● GROUP/COGROUP, DISTINCT ● Record/Array FLATTEN ● Java UDFs, Python UDFs ● Pig Nested FOREACH and sort/limit (in Windows) ● Hive Not yet

Under the hood of the nearline architecture

Pig to Samza through SQL processing Open source framework for building dynamic data management systems. Including: ➢ SQL Parser ➢ Relational algebra APIs ➢ Query planning engine We built UMP nearline with: ➢ Pig’s Grunt parser ➢ Calcite relational algebra ➢ Calcite query planning engine

Architecture ... Metric union User code User code Dimension augmentation Calcite relational algebra as an IR convert generate Samza code optimize Samza physical plan Samza configuration Pig to Calcite Calcite to Samza

Pig to Calcite # code LOAD … LOAD ... COGROUP ... STORE … GruntParser CO- GROUP LOAD LOAD PigRelConverter FULL OUTERJ OIN AGGRE GATE AGGRE GATE TABLE SCAN TABLE SCAN PRO- JECT User scripts Pig Logical Plan Calcite relational algebra

Example INNER JOIN FILTER FILTER PROJECT PROJECT PROJECT TABLE SCAN TABLE SCAN Calcite logical plan

Planning/Optimization ➢ Calcite logical plans: ○ Relational algebra: What to do ➢ Samza physical plans: ○ Samza physical node: How to do it ➢ Calcite Samza planner: ○ Calcite logical plan -> optimized Samza physical plan

Example Stream- Stream Self Join Samza Project Samza Project Samza Filter Samza Filter Samza Project Input Stream INNER JOIN FILTER FILTER PROJECT PROJECT PROJECT TABLE SCAN TABLE SCAN Calcite Samza planner Calcite logical plan Samza physical plan

Code-gen From Samza physical plans: ➢ Generate Samza code for constructing the stream graph using Samza Fluent APIs . Mapping: ➢ Samza physical nodes -> corresponding stream APIs: ○ Samza project -> stream.map() ○ Samza filter -> stream.filter() ○ ... ➢ Relational expressions -> lamba functions: ○ Filter expressions -> filter() functions ○ Project expressions -> map() functions ○ ...

Example Schema and UDF declarations Operator mapping Filter functions Map functions Produce to Kafka ...

Config-gen Stream Stream Join Samza Project Samza Project Samza Filter Samza Filter Samza Project Input Stream # dataset.conf app-src app-def

Nearline production use case - Storylines

Conquering the Lambda architecture in LinkedIn metrics platform with Apache Calcite and Apache Samza

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