SQL for NoSQL and how Apache Calcite can help
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The document discusses the integration of SQL with NoSQL databases and the role of Apache Calcite in optimizing queries across different data storage systems. It highlights the challenges posed by diverse data models and the need for unified query languages in mixed data environments. The presentation outlines how Calcite serves as a framework to facilitate SQL interfaces and query optimizations for various NoSQL technologies.
![“Standard” Data Process/Query Language?
7
• Functional - Unified Programming
Model
• Apache {Beam, Spark, Flink,
Apex, Crunch}, Cascading
• Converging around Apache
Beam
• Declarative - SQL
• Adopted by many NoSQL
Vendors
• Most Hadoop tasks: Hive and
SQL-on-Hadoop
• Spark SQL - most used
production component for 2016
• Google F1
pcollection.apply(Read.from(”in.txt"))
.apply(FlatMapElements.via((String word) ->
asList(word.split("[^a-zA-Z']+")))
.apply(Filter.by((String word)->!word.isEmpty()))
.apply(Count.<String>perElement())
SELECT b."totalPrice", c."firstName”
FROM "BookOrder" as b
INNER JOIN "Customer" as c
ON b."customerNumber" = c."customerNumber”
WHERE b."totalPrice" > 0;
Batch & Streaming, OLTP
OLAP, EDW, Exploration](https://image.slidesharecdn.com/fosdem2017christiantzolov-170206202927/85/SQL-for-NoSQL-and-how-Apache-Calcite-can-help-7-320.jpg)
![Simple SQL Adapter
16
<<SchemaFactory>>
MySchemaFactory
+create(operands):Schema
<<create>>
<<ScannableTable>>
MyTable
+getRowType(RelDataTypeFactor)
+scan(ctx):Ennumerator<Object[]>
<<Schema>>
MySchema
+getTableMap():Map<String, Table>)
<<on scan() create>>
<<Enummerator>>
MyEnummerator
+moveNext()
+convert(Object):E
My NoSQL
<<create>>
<<Get all Data>>
defaultSchema: 'MyNoSQL',
schemas: [{
name: ’MyNoSQLAdapter,
factory: MySchemaFactory’,
operand: { myNoSqlUrl: …, }
}]
!connect jdbc:calcite:model=path-to-model.json
Returns an Enumeration
over the entire target
data store
Uses reflection to builds
RelDataType from your
value’s class type
Converts MyNoSQL
value response into
Calcite row data
Defined in the Linq4j
sub-project
ScannableTable,
FilterableTable,
ProjectableFilterableTable
Initialize
Query
SELECT b."totalPrice”
FROM "BookOrder" as b
WHERE b."totalPrice" > 0;](https://image.slidesharecdn.com/fosdem2017christiantzolov-170206202927/85/SQL-for-NoSQL-and-how-Apache-Calcite-can-help-16-320.jpg)
![Non-Relational Tables (Simple)
17
Scanned without intermediate relational expression.
• ScannableTable - can be scanned
• FilterableTable - can be scanned, applying supplied filter expressions
• ProjectableFilterableTable - can be scanned, applying supplied filter expressions
and projecting a given list of columns
Enumerable<Object[]> scan(DataContext root, List<RexNode> filters, int[] projects);
Enumerable<Object[]> scan(DataContext root, List<RexNode> filters);
Enumerable<Object[]> scan(DataContext root);](https://image.slidesharecdn.com/fosdem2017christiantzolov-170206202927/85/SQL-for-NoSQL-and-how-Apache-Calcite-can-help-17-320.jpg)
![Relational Algebra
23
Scan Scan
Join
Filter
Project
Customer [c] BookOrder [b]
(on customerNumber)
(b.totalPrice > 0)
(c.firstName, b.totalPrice)
SELECT b."totalPrice", c."firstName”
FROM "BookOrder" as b
INNER JOIN "Customer" as c ON b."customerNumber" = c."customerNumber”
WHERE b."totalPrice" > 0;
Scan Scan
Join
Project
Customer [c] BookOrder [b]
(on customerNumber)
(totalPrice > 0)
(c.firstName, b.totalPrice)
Project(firstName,
customerNumber)
Filter
(totalPrice,
customerNumber)Project
optimize](https://image.slidesharecdn.com/fosdem2017christiantzolov-170206202927/85/SQL-for-NoSQL-and-how-Apache-Calcite-can-help-23-320.jpg)
SQL for NoSQL and how Apache Calcite can help
- 1. SQL for NoSQL and how Apache Calcite can help FOSDEM 2017
- 2. Christian Tzolov 2 Engineer at Pivotal BigData, Hadoop, Spring Cloud Dataflow Apache Committer, PMC member Apache {Crunch, Geode, HAWQ, ...} Disclaimer This talk expresses my personal opinions. It is not read or approved by Pivotal and does not necessarily reflect the views and opinions of Pivotal nor does it constitute any official communication of Pivotal. Pivotal does not support any of the code shared here. blog.tzolov.net twitter.com/christzolov nl.linkedin.com/in/tzolov
- 3. 3 “It will be interesting to see what happens if an established NoSQL database decides to implement a reasonably standard SQL; The only predictable outcome for such an eventuality is plenty of argument.” 2012, Martin Fowler, P.J.Sadalage, NoSQL Distilled
- 5. NoSQL Driving Forces 5 • Infrastructure Automation and Elasticity (Cloud Computing) • Rise of Internet Web, Mobile, IoT – Data Volume, Velocity, Variety challenges • Row-based Relational Model. Object-Relational Impedance Mismatch ACID & 2PC clash with Distributed architectures. CAP, PAXOS instead.. More convenient data models: Datastores, Key/Value, Graph, Columnar, Full-text Search, Schema-on-Load… Eliminate operational complexity and cost. Shift from Integration to application databases …
- 6. Data Big Bang Implications 6 • Over 150 commercial NoSQL and BigData systems. • Organizations will have to mix data storage technologies! • How to integrate such multitude of data systems?
- 7. “Standard” Data Process/Query Language? 7 • Functional - Unified Programming Model • Apache {Beam, Spark, Flink, Apex, Crunch}, Cascading • Converging around Apache Beam • Declarative - SQL • Adopted by many NoSQL Vendors • Most Hadoop tasks: Hive and SQL-on-Hadoop • Spark SQL - most used production component for 2016 • Google F1 pcollection.apply(Read.from(”in.txt")) .apply(FlatMapElements.via((String word) -> asList(word.split("[^a-zA-Z']+"))) .apply(Filter.by((String word)->!word.isEmpty())) .apply(Count.<String>perElement()) SELECT b."totalPrice", c."firstName” FROM "BookOrder" as b INNER JOIN "Customer" as c ON b."customerNumber" = c."customerNumber” WHERE b."totalPrice" > 0; Batch & Streaming, OLTP OLAP, EDW, Exploration
- 8. SQL for NoSQL? 8 • Extended Relational Algebra - already present in most NoSql data system • Relational Expression Optimization – Desirable but hard to implement
- 9. Organization Data - Integrated View 9 Single Federated DB (M:1:N) HAWQ FDBS NoSQL 1 PXF 1 Native API 1 Apache HAWQ Optimizer, Columnar (HDFS) Organization Data Tools SQL/JDBC NoSQL 1 PXF 2 Native API 2 NoSQL n PXF n Native API n … Organization Data Tools NoSQL 1 Calcite SQLAdapter 1 SQL/JDBC NoSQL 2 Calcite SQLAdapter 2 SQL/JDBC NoSQL n Calcite SQLAdapter n SQL/JDBC … Direct (M:N) https://issues.apache.org/jira/browse/HAWQ-1235
- 10. Single Federated Database 10 Federated External Tables with Apache HAWQ - MPP, Shared-Noting, SQL- on-Hadoop CREATE EXTERNAL TABLE MyNoSQL ( customer_id TEXT, first_name TEXT, last_name TEXT, gender TEXT ) LOCATION ('pxf://MyNoSQL-URL>? FRAGMENTER=MyFragmenter& ACCESSOR=MyAccessor& RESOLVER=MyResolver&') FORMAT 'custom'(formatter='pxfwritable_import');
- 11. Apache Calcite? Java framework that allows SQL interface and advanced query optimization, for virtually any data system • Query Parser, Validator and Optimizer(s) • JDBC drivers - local and remote • Agnostic to data storage and processing
- 12. Calcite Application 12 • Apache Apex • Apache Drill • Apache Flink • Apache Hive • Apache Kylin • Apache Phoenix • Apache Samza • Apache Storm • Cascading • Qubole Quark • SQL-Gremlin … • Apache Geode
- 13. SQL Adapter Design Choices 13 SQL completeness vs. NoSql design integrity (simple) Predicate Pushdown: Scan, Filter, Projection (complex) Custom Relational Rules and Operations: Sort, Join, GroupBy ... Catalog – namespaces accessed in queries Schema - collection of schemas and tables Table - single data set, collection of rows RelDataType – SQL fields types in a Table • Move Computation to Data • Data Type Conversion
- 14. Geode to Calcite Data Types Mapping 14 Geode Cache Region 1 Region K ValKey v1k1 v2k2 … Calcite Schema Table 1 Table K Col1 Col2 ColN V(M,1)RowM V(M,2) V(M,N) V(2,1)Row2 V(2,2) V(2,N) V(1,1)Row1 V(1,2) V(1,N) … Regions are mapped into Tables Geode Cache is mapped into Calcite Schema Geode Key/Value is mapped into Table Row Create Column Types (RelDataType) from Geode Value class (JavaTypeFactoryImpl)
- 15. Geode Adapter - Overview Geode API and OQL SQL/JDBC/ODBC Convert SQL relational expressions into OQL queries Geode Adapter (Geode Client) Geode ServerGeode ServerGeode Server Data Data Data Push down the relational expressions supported by Geode OQL and falls back to the Calcite Enumerable Adapter for the rest Enumerable Adapter Apache Calcite Spring Data Geode Spring Data API for interacting with Geode Parse SQL, converts into relational expression and optimizes
- 16. Simple SQL Adapter 16 <<SchemaFactory>> MySchemaFactory +create(operands):Schema <<create>> <<ScannableTable>> MyTable +getRowType(RelDataTypeFactor) +scan(ctx):Ennumerator<Object[]> <<Schema>> MySchema +getTableMap():Map<String, Table>) <<on scan() create>> <<Enummerator>> MyEnummerator +moveNext() +convert(Object):E My NoSQL <<create>> <<Get all Data>> defaultSchema: 'MyNoSQL', schemas: [{ name: ’MyNoSQLAdapter, factory: MySchemaFactory’, operand: { myNoSqlUrl: …, } }] !connect jdbc:calcite:model=path-to-model.json Returns an Enumeration over the entire target data store Uses reflection to builds RelDataType from your value’s class type Converts MyNoSQL value response into Calcite row data Defined in the Linq4j sub-project ScannableTable, FilterableTable, ProjectableFilterableTable Initialize Query SELECT b."totalPrice” FROM "BookOrder" as b WHERE b."totalPrice" > 0;
- 17. Non-Relational Tables (Simple) 17 Scanned without intermediate relational expression. • ScannableTable - can be scanned • FilterableTable - can be scanned, applying supplied filter expressions • ProjectableFilterableTable - can be scanned, applying supplied filter expressions and projecting a given list of columns Enumerable<Object[]> scan(DataContext root, List<RexNode> filters, int[] projects); Enumerable<Object[]> scan(DataContext root, List<RexNode> filters); Enumerable<Object[]> scan(DataContext root);
- 18. Calcite Ecosystem 18 Several “semi-independent” projects. JDBC and Avatica Linq4j Expression Tree Enumerable Adapter Relational • Relational Expressions • Row Expression • Optimization Rules • Planner … SQL Parser & AST Port of LINQ (Language-Integrated Query) to Java. Local and Remote JDBC driver Converts SQL queries Into AST (SqlNode …) 3rd party Adapters Method for translating executable code into data (LINQ/MSN port) Default (In-memory) Data Store Adapter implementation. Leverages Linq4j Relational Algebra, expression, optimizations … Interpreter Complies Java code generated from linq4j “Expressions”. Part of the physical plan implementer
- 19. Calcite SQL Query Execution Flow 19 Enumerable Interpreter Prepare SQL, Relational, Planner Geode Adapter Binder JDBC Geode Cluster 1 2 3 4 5 6 7 7 7 2. Parse SQL, convert to rel. expressions. Validate and Optimize them 3. Start building a physical plan from the relation expressions 4. Implement the Geode relations and encode them as Expression tree 5. Pass the Expression tree to the Interpreter to generate Java code 6. Generate and Compile a Binder instance that on ‘bind()’ call runs Geodes’ query method 1. On new SQL query JDBC delegates to Prepare to prepare the query execution 7. JDBC uses the newly compiled Binder to perform the query on the Geode Cluster Calcite Framework Geode Adapter 2
- 20. Calcite Relational Expressions 20 RelNode Relational expression TableScan Project Filter Aggregate Join Intersect Sort RexlNode Row-level expressions Project, Sort fields Filter, Join conditions Input Column Ref Literal Struct field access Function call Window expressions * RelTrait * Physical attribute of a relation
- 21. Calcite Relational Expressions 21 RelNode + register(RelOptPlander) + List<RelNode> getInputs(); RelOptPlanner +findBestExp():RelNode RexNode RelTrait Convention NONE * * EnumberableConvention RelOptRule + onMatch(call) <<register>> <<create>> MyDBConvention ConverterRule + RelNode convert(RelNode) Converts from one calling convention to another Convertor Indicate that it converts a physical attribute only! <<rules>> * <<inputs>> * <<root>> Query optimizer: Transforms a relational expression according to a given set of rules and a cost model. RelOptCluster Rule transforms an expression into another. It has a list of Operands, which determine whether the rule can be applied to a particular section of the tree.RelOptRuleOperand *<<fire criteria>> Calling convention used to represent a single data source. Inputs to a relational expression must be in the same convention
- 22. Calcite Adapter Implementation Patterns 22 MyAdapterRel + implement(implContext) MyAdapterConvention Convention.Impl(“MyAdapter”) Common interface for all MyAdapter Relation Expressions. Provides implementation callback method called as part of physical plan implementation ImplContext + implParm1 + implParm2 … RelNode MyAdapterTable + toRel(optTable) + asQueryable(provider,…) MyAdapterQueryable + myQuery(params) : Enumetator TranslatableTable <<instance of>> AbstractQueryableTable AbstractTableQueryable <<create>> Can convert queries in Expression myQuery() implements the call to your DB It is called by the auto generated code. It must return an Enumberable instance MyAdapterScan + register(planer) { Registers all MyAdapter Rules } <<create>> MyAdapterToEnumerableConvertorRule operands: (RelNode.class, MyAdapterConvention, EnumerableConvention) ConverterRue TableScan MyAdapterToEnumerableConvertor + implement(EnumerableRelImplementor) { ctx = new MyAdapterRel.ImplContext() getImputs().implement(ctx) return BlockBuild.append( MY_QUERY_REF, Expressions.constant(ctx.implParms1), Expressions.constant(ctx.implParms2) … EnumerableRel ConvertorImpl <<create on match >> MyAdapterProject MyAdapterFilter MyAdapterXXX RelOptRule MyAdapterProjectRu MyAdapterFilterRule MyAdapterXXXRule <<create on match >> Recursively call the implement on each MyAdapter Relation Expression Encode the myQuery(params) call as Expressions MY_QUERY_REF = Types.lookupMethod( MyAdapterQueryable.class, ”myQuery”, String.class String.class); 1 3 4 5 2 6 7 8 9 Calcite Framework MyAdapter components
- 23. Relational Algebra 23 Scan Scan Join Filter Project Customer [c] BookOrder [b] (on customerNumber) (b.totalPrice > 0) (c.firstName, b.totalPrice) SELECT b."totalPrice", c."firstName” FROM "BookOrder" as b INNER JOIN "Customer" as c ON b."customerNumber" = c."customerNumber” WHERE b."totalPrice" > 0; Scan Scan Join Project Customer [c] BookOrder [b] (on customerNumber) (totalPrice > 0) (c.firstName, b.totalPrice) Project(firstName, customerNumber) Filter (totalPrice, customerNumber)Project optimize
- 24. Calcite with Geode - Without Implementation 24 SELECT b."totalPrice", c."firstName” FROM "BookOrder" as b INNER JOIN "Customer" as c ON b."customerNumber" = c."customerNumber” WHERE b."totalPrice" > 0;
- 25. Calcite with Geode – Scannable Table (Simple) 25 SELECT b."totalPrice", c."firstName” FROM "BookOrder" as b INNER JOIN "Customer" as c ON b."customerNumber" = c."customerNumber” WHERE b."totalPrice" > 0;
- 26. Calcite with Geode – Relational (Complex) 26 SELECT b."totalPrice", c."firstName” FROM "BookOrder" as b INNER JOIN "Customer" as c ON b."customerNumber" = c."customerNumber” WHERE b."totalPrice" > 0;
- 28. References 28 • Big Data is Four Different Problems, 2016, M.Stonebraker: https://www.youtube.com/watch?v=S79-buNhdhI • Turning Database Inside-Out, 2015 (M. Kleppmann) https://www.confluent.io/blog/turning-the-database-inside-out-with-apache-samza • NoSQL Distilled, 2012 (Pramod J. Sadalage and M.Fowler) https://martinfowler.com/books/nosql.html • Architecture of a Database System, 2007 (J.M. Hellerstein, M. Stonebraker, J. Hamilton)http://db.cs.berkeley.edu/papers/fntdb07-architecture.pdf • ORCA: A Modular Query Optimizer Architecture for Big Data: http://15721.courses.cs.cmu.edu/spring2016/papers/p337-soliman.pdf • Apache Geode Project (2016) : http://geode.apache.org • Geode Object Query Language (OQL) : http://bit.ly/2eKywgp • Apache Calcite Project (2016) : https://calcite.apache.org • Apache Geode Adapter for Apache Calcite: https://github.com/tzolov/calcite • Relational Algebra Operations: https://www.coursera.org/learn/data-manipulation/lecture/ 4JKs1/relational-algebra-operators-union-difference-selection
- 29. Thanks!
- 30. Apache Geode? “… in-memory, distributed database with strong consistency built to support low latency transactional applications at extreme scale”
- 31. Why Apache Geode? 31 5,700 train stations 4.5 million tickets per day 20 million daily users 1.4 billion page views per day 40,000 visits per second 7,000 stations 72,000 miles of track 23 million passengers daily 120,000 concurrent users 10,000 transactions per minute https://pivotal.io/big-data/case-study/distributed-in-memory-data-management-solution https://pivotal.io/big-data/case-study/scaling-online-sales-for-the-largest-railway-in-the-world-china-railway-corporation China Railway
- 32. Geode Features 32 • In-Memory Data Storage – Over 100TB Memory – JVM Heap + Off Heap • Any Data Format – Key-Value/Object Store • ACID and JTA Compliant Transactions • HA and Linear Scalability • Strong Consistency • Streaming and Event Processing – Listeners – Distributed Functions – Continuous OQL Queries • Multi-site / Inter-cluster • Full Text Search (Lucene indexes) • Embedded and Standalone • Top Level Apache Project
- 33. Apache Geode Concepts Cache Server (member) Cache Region 1 Region N ValKe y v1k1 v2k2 … Cache - In-memory collection of Regions Region - consistent, distributed Map (key-value), Partitioned or Replicated CacheServer – process connected to the distributed system with created Cache ClientLocator (member) Client –read and modify the content of the distributed system Locator – tracks system members and provides membership information … Listeners Functions Functions – distributed, concurrent data processing Listener – event handler. Registers for one or more events and notified when they occur
- 34. Geode Topology Cache ServerCache ServerCache Server Cache Data Cache Data Cache Data Peer-to-Peer Cache ServerCache ServerCache Server Cache Data Cache Data Cache Data Client Local Cache pool Client-Server Cache Server Cache Server Gateway Sender … Cache Server Gateway Receiver Cache ServerCache Server Cache Data Cache Data Cache Data Cache Data Gateway Receiver Cache Server … Gateway Sender Cache Server Cache Server Cache Data Cache Data Cache Data Cache Data WAN Multi-site Boundary Multi-Site