Spark and Couchbase– Augmenting the Operational Database with Spark
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How do NoSQL Document-Oriented Databases like Couchbase fit in with Apache Spark? This set of slides gives a couple of use cases, shows why Couchbase works great with Spark, and sets up a scenario for a demo.
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Spark and Couchbase– Augmenting the Operational Database with Spark
- 1. SPARK AND COUCHBASE: AUGMENTING THE OPERATIONAL DATABASE WITH SPARK Matt Ingenthron Couchbase
- 2. Agenda Why integrate Spark and NoSQL? Architectural alignment Integration “Points of Interest” Automatic sharding and data locality Streams: Data Replication and Spark Streaming Predicate pushdown and global indexing Flexible schemas and schema inference See it in action
- 3. Why Spark and NoSQL?
- 4. NoSQL + Spark use cases Operations Analysis NoSQL Recommendations Next gen data warehousing Predictive analytics Fraud detection Catalog Customer 360 + IOT Personalization Mobile applications
- 5. Big Data at a Glance Couchbase Spark Hadoop Use cases • Operational • Web / Mobile • Analytics • Machine Learning • Analytics • Machine Learning Processing mode • Online • Ad Hoc • Ad Hoc • Batch • Streaming (+/-) • Batch • Ad Hoc (+/-) Low latency = < 1 ms ops Seconds Minutes Performance Highly predictable Variable Variable Users are typically… Millions of customers 100’s of analysts or data scientists 100’s of analysts or data scientists Memory-centric Memory-centric Disk-centric Big data = 10s of Terabytes Petabytes Petabytes ANALYTICALOPERATIONAL
- 6. Use Case: Operationalize Analytics / ML Hadoop Examples: recommend content and products, spot fraud or spam Data scientists train machine learning models Load results into Couchbase so end users can interact with them online Machine Learning Models Data Warehouse Historical Data NoSQL
- 7. Use Case: Operationalize ML Model NoSQL node(e.g.) Training Data (Observations) Serving Predictions
- 8. Spark connects to everything… DCP KV N1QL Views Adapted from: Databricks – Not Your Father’s Database https://www.brighttalk.com/webcast/12891/196891
- 9. Use Case #2: Data Integration RDBMSs3hdfs Elasticsearc h Data engineers query data in many systems w/ one language & runtime Store results where needed for further use Late binding of schemas NoSQL
- 11. Full Text Search Search for and fetch the most relevant records given a freeform text string Key-Value Directly fetch / store a particular record Query Specify a set of criteria to retrieve relevant data records. Essential in reporting. Map-Reduce Views Maintain materialized indexes of data records, with reduce functions for aggregation Data Streaming Efficiently, quickly stream data records to external systems for further processing or integration
- 12. Hash Partitioned Data Auto Sharding – Bucket And vBuckets A bucket is a logical, unique key space Each bucket has active & replica data sets Each data set has 1024 virtual buckets (vBuckets) Each vBucket contains 1/1024th of the data set vBuckets have no fixed physical server location Mapping of vBuckets to physical servers is called the cluster map Document IDs (keys) always get hashed to the same vBucket Couchbase SDK’s lookup the vBucket server mapping
- 13. N1QL Query N1QL, pronounced “nickel”, is a SQL service with extensions specifically for JSON Is stateless execution, however… Uses Couchbase’s Global Secondary Indexes. These are sorted structures, range partitioned. Both can run on any nodes within the cluster. Nodes with differing services can be added and removed as needed.
- 14. MapReduce Couchbase Views A JavaScript based, incremental Map-Reduce service for incrementally building sorted B+Trees. Runs on every node, local to the data on that node, stored locally. Automatically merge-sorted at query time.
- 15. Data Streaming with DCP A general data streaming service, Database Change Protocol. Allows for streaming all data out and continuing, or… Stream just what is coming in at the time of connection, or… Stream everything out for transfer/takeover…
- 17. Key-Value Direct fetching/storing of a particular record. Query Specifying a set of criteria to retrieve relevant data records. Essential in reporting. Map-Reduce Views Maintain materialized indexes of data records, with reduce functions for aggregation. Data Streaming Efficiently, quickly stream data records to external systems for further processing or integration. Full Text Search Search for, and allow tuning of the system to fetch the most relevant records given a freeform search string.
- 18. Automatic sharding and data locality Integration Points of Interest
- 19. What happens in Spark Couchbase KV When 1 Spark node per CB node, the connector will use the cluster map and push down location hints Helpful for situations where processing is intense, like transformation Uses pipeline IO optimization However, not available for N1QL or Views Round robin - can’t give location hints Back end is scatter gather with 1 node responding
- 20. Predicate pushdown and global indexing Integration Points of Interest
- 21. SparkSQL on N1QL with Global Secondary Indexes TableScan Scan all of the data and return it PrunedScan Scan an index that matches only relevant data to the query at hand. PrunedFilteredScan Scan an index that matches only relevant data to the query at hand.
- 22. Couchbase Cluster B-Tree (Global Secondary Index) Data Data Data Data Query Application Client
- 23. Couchbase Cluster B-Tree (Global Secondary Index) Data Data Data Data Query Application Client
- 25. Predicate pushdown Notes from implementing: Spark assumes it’s getting all the data, applies the predicates Future potential optimizations Push down all the things! Aggregations JOINs Looking at Catalyst engine extensions from SAP But, it’s not backward compatible and… …many data sources can only push down filters image courtesy http://allthefreethings.com/about/
- 26. Streams: Data Replication and Spark Streaming Integration Points of Interest
- 27. DCP and Spark Streaming Many system architectures rely upon streaming from the ‘operational’ data store to other systems Lambda architecture => store everything and process/reprocess everything based on access Command Query Responsibility Segregation - (CQRS) Other reactive pattern derived systems and frameworks
- 28. Documents flow into the system from outside Documents are then streamed down to consumers In most common cases, flows memory to memory DCP and Spark Streaming Couchbase Node DCP Consumer Other Cluster Nodes Spark Cluster
- 29. See it in Action
- 30. Couchbase Spark Connector 1.2 • Spark 1.6 support, including Datasets • Full DCP flow control support • Enhanced Java APIs • Bug fixes 34
- 31. Questions?
- 32. THANK YOU. @ingenthr Try Couchbase Spark Connector 1.2 http://www.couchbase.com/bigdata
- 34. Deployment Topology Couchbase Spark Worker Couchbase Couchbase Many small gets Streaming with low mutation rate Ad hoc Couchbase Data Node Data Node Spark Worker Couchbase Couchbase Medium processing Predictable workloads Plenty of overhead on machines Couchbase Couchbase Couchbase Couchbase Couchbase Couchbase Data Node Data Node Spark Worker XDCR Heaviest processing Workload isolation writes
Editor's Notes
- CHECK THE ORDER
- Analysis side – includes various types of machine learning and analytics Often the data warehousing includes many different sources of data
- This is a popular use case for Spark because it provides the ability to go big and serve your predictions to a lot of users
- Generally speaking, there’s a thin layer like node.js that gets JSON from the NoSQL system and feeds it to the user. Imagine that user is doing something like shopping or listening to music. As they use the App, you need ridiculously low latency because they system has to do something based on that data. But you also want to send that user’s actions back to the Spark system or wherever .
- Couchbase advantages include: Fast: memory-centric, integrated cache, implicit batching from the SDK with async & flat map Dev Convenience: Native SDKs, automatic cluster management, code your app without reference to infrastructure Sophisticated: Query using SQL for JSON (N1QL), supports JOINs
- This is like federation
- TODO: Come up with a better title here. Goal is to lay out the services that are part of Couchbase and then talk about how they fit in with a spark deployment.
- Add lots of notes
- TODO: Merge ideas from the next slide (hidden)
- TODO: fix the colors
- On a given Spark worker node, we can optimize Internal to the Couchbase JVM Core, we pipeline operations Amortize the responses over many operations, meaning no cost for successful operations Efficient scheduling
- Future: use the union or differences of indexes for filtering down candidates. More powerful than traditional relational databases owing to indexing architecture.
- Surprise! We can push down predicates with an O(log n) lookup 2i – super awesome, that’s how awesome we are Relational DBs do this, and they’re not expecting this to be good. Instead, go to every node and go full retard
- Surprise! We can push down predicates with an O(log n) lookup 2i – super awesome, that’s how awesome we are Relational DBs do this, and they’re not expecting this to be good. Instead, go to every node and go full retard
- Spark always applies the predicates Spark expects it will always get the data and apply the filters If you’re building a similar system, turn off the flag so Spark doesn’t do needlessly re-apply the filters
- Optional but cool PERFORMANCE IMPLICATIONS!!! OMG KAFKA is most common Matt could show his mad science demo Limitation - Only backfill works, can’t do from point in time. This is a bug.
- Can’t currently shard across spark workers since there’s no way to see this topology
- Fully transparent cluster and bucket management, including direct access if needed