SQLFire is a scalable SQL database that provides an alternative to NoSQL databases by using SQL instead. It features hash partitioning, entity groups to colocate related data, and data aware stored procedures that can run queries in parallel across partitions. SQLFire uses a tunable consistency model and supports distributed transactions without global locks. Data is stored primarily in memory but can optionally be persisted to disk in parallel for high throughput writes without seeks. Benchmark results showed SQLFire can scale linearly from 2 to 10 servers and 200 to 1200 clients.

1. SQLFire Scalable SQL instead of NoSQL Jags Ramnarayan Chief Architect, GemFire Products Jags Ramnarayan

2. Agenda Various NoSQL attributes and why SQL SQLFire features + Demo Scalability patterns Hash partitioning Entity groups and collocation Scaling behavior using “data aware stored procedures” Consistency model How we do distributed transactions Shared nothing persistence

4. Design roots don’t necessarily apply today

5. Too much focus on ACID

6. Disk synchronization bottlenecksConfidential

7. “Shared nothing” commodity clusters focus shifts to memory, distributing data and clustering Scale by partitioning the data and move behavior to data nodes HA within cluster and across data centers Add capacity to scale dynamically Common themes in next-gen DB architectures 4 NoSQL, Data Grids, Data Fabrics, NewSQL Confidential

9. Key-value

10. Column family (inspired by Google BigTable)

11. Document

12. Graph

13. Most focus on making model less rigid than SQL

14. Consistency model is not ACIDLow scale Very high scale High scale Tunable Consistency Eventual STRICT – Full ACID (RDB) 5

15. What is our take with SQLFire? Eventual consistency is too difficult for the average developer Write(A,1)  Read(A) may return 2 or (1,2) SQL : Flexible, easily understood, strong type system essential for integrity as well as query engine efficiency

16. SQLFire Replicated, partitioned tables in memory. Redundancy through memory copies. Data resides on disk when you explicitly say so Powerful SQL engine: standard SQL for select, DML DDL has SQLF extensions Leverages GemFire data grid engine.

17. SQLFire Applications access the distributed DB using JDBC, ADO.NET Consistency model is FIFO, Tunable Distributed transactions without global locks

18. SQLFire Asynchronous replication over WAN Synchronous replication within cluster Clients failover, failback Easily integrate with existing DBs - caching framework to read through, write through or write behind

19. SQLFire When nodes are added, data and behavior is rebalanced without blocking current clients "Data aware procedures“ - standard Java stored procedures with "data aware" and parallelism extensions

20. Flexible Deployment Topologies Java Application cluster can host an embedded clustered database by just changing the URL jdbc:sqlfire:;mcast-port=33666;host-data=true Confidential 11

21. Flexible Deployment Topologies Confidential 12

22. Partitioning & Replication

23. Explore features through example Assume, thousands of flight rows, millions of flightavailability records

24. SQLF Creating Tables CREATE TABLE FLIGHTS ( FLIGHT_ID CHAR(6) NOT NULL PRIMARY KEY, SEGMENT_NUMBER INTEGER NOT NULL , ORIG_AIRPORT CHAR(3), DEPART_TIME TIME, … ) ; Hash partitioned on PK by default Table Partitioned Table Partitioned Table Partitioned Table SQLF SQLF SQLF

25. CREATE TABLE FLIGHTAVAILABILITY ( FLIGHT_ID CHAR(6) NOT NULL , SEGMENT_NUMBER INTEGER NOT NULL , FLIGHT_DATE DATE NOT NULL , ECONOMY_SEATS_TAKEN INTEGER DEFAULT 0, …) PARTITION BY COLUMN (FLIGHT_ID) COLOCATE WITH (FLIGHTS) CREATE TABLE FLIGHTS ( FLIGHT_ID CHAR(6) NOT NULL , SEGMENT_NUMBER INTEGER NOT NULL , ORIG_AIRPORT CHAR(3), DEPART_TIME TIME, …) PARTITION BY COLUMN (FLIGHT_ID); CREATE TABLE Airlines AIRLINE CHAR(2) NOT NULL PRIMARY KEY, AIRLINE_FULL VARCHAR(24), BASIC_RATE DOUBLE PRECISION, DISTANCE_DISCOUNT DOUBLE PRECISION,…. ) CREATE TABLE FLIGHTS ( FLIGHT_ID CHAR(6) NOT NULL , SEGMENT_NUMBER INTEGER NOT NULL , ORIG_AIRPORT CHAR(3), DEPART_TIME TIME, …) PARTITION BY COLUMN (FLIGHT_ID)REDUNDANCY 1; CREATE TABLE Airlines AIRLINE CHAR(2) NOT NULL PRIMARY KEY, AIRLINE_FULL VARCHAR(24), BASIC_RATE DOUBLE PRECISION, DISTANCE_DISCOUNT DOUBLE PRECISION,…. ) REPLICATE; Replicated Table Replicated Table Replicated Table Table Redundant Partition Redundant Partition Partitioned Table Partitioned Table Redundant Partition Partitioned Table SQLF SQLF SQLF SQLF Creating Tables Colocated Partition Colocated Partition Colocated Partition

26. CREATE TABLE Airlines AIRLINE CHAR(2) NOT NULL PRIMARY KEY, AIRLINE_FULL VARCHAR(24), BASIC_RATE DOUBLE PRECISION, DISTANCE_DISCOUNT DOUBLE PRECISION,…. ) Table SQLF SQLF SQLF SQLF Creating Tables

27. CREATE TABLE Airlines AIRLINE CHAR(2) NOT NULL PRIMARY KEY, AIRLINE_FULL VARCHAR(24), BASIC_RATE DOUBLE PRECISION, DISTANCE_DISCOUNT DOUBLE PRECISION,…. ) REPLICATE; Replicated Table Replicated Table Replicated Table SQLF SQLF SQLF SQLF Creating Tables

28. SQLF Creating Tables CREATE TABLE FLIGHTS ( FLIGHT_ID CHAR(6) NOT NULL , SEGMENT_NUMBER INTEGER NOT NULL , ORIG_AIRPORT CHAR(3), DEPART_TIME TIME, PARTITION BY COLUMN (FLIGHT_ID); Table Replicated Table Replicated Table Replicated Table Partitioned Table Partitioned Table Partitioned Table SQLF SQLF SQLF

29. CREATE TABLE FLIGHTS ( FLIGHT_ID CHAR(6) NOT NULL , SEGMENT_NUMBER INTEGER NOT NULL , ORIG_AIRPORT CHAR(3), DEPART_TIME TIME, …) PARTITION BY COLUMN (FLIGHT_ID)REDUNDANCY 1; Table Replicated Table Replicated Table Replicated Table Partitioned Table Partitioned Table Partitioned Table Redundant Partition Redundant Partition Redundant Partition SQLF SQLF SQLF SQLF Creating Tables

30. CREATE TABLE FLIGHTAVAILABILITY ( FLIGHT_ID CHAR(6) NOT NULL , SEGMENT_NUMBER INTEGER NOT NULL , FLIGHT_DATE DATE NOT NULL , ECONOMY_SEATS_TAKEN INTEGER DEFAULT 0, …) PARTITION BY COLUMN (FLIGHT_ID) COLOCATE WITH (FLIGHTS) Table Replicated Table Replicated Table Replicated Table Partitioned Table Partitioned Table Partitioned Table Colocated Partition Colocated Partition Colocated Partition Redundant Partition Redundant Partition Redundant Partition SQLF SQLF SQLF SQLF Creating Tables

31. By default, it is only the data dictionary that is persisted to disk. Table Replicated Table Replicated Table Replicated Table Partitioned Table Partitioned Table Partitioned Table Colocated Partition Colocated Partition Colocated Partition Redundant Partition Redundant Partition Redundant Partition SQLF SQLF SQLF SQLF Creating Tables

32. CREATE TABLE FLIGHTAVAILABILITY ( FLIGHT_ID CHAR(6) NOT NULL , SEGMENT_NUMBER INTEGER NOT NULL , FLIGHT_DATE DATE NOT NULL , ECONOMY_SEATS_TAKEN INTEGER DEFAULT 0, …) PARTITION BY COLUMN (FLIGHT_ID) COLOCATE WITH (FLIGHTS) PERSISTENT ; Table Replicated Table Replicated Table Replicated Table Partitioned Table Partitioned Table Partitioned Table Redundant Partition Redundant Partition Redundant Partition SQLF SQLF SQLF SQLF Creating Tables Colocated Partition Colocated Partition Colocated Partition

33. Partitioning Options To partition using the Primay Key, use: (Primary Key’s Java implementation must hash evenly across its range) PARTITION BY PRIMARY KEY CREATE TABLE FLIGHTS ( FLIGHT_ID CHAR(6) NOT NULL , SEGMENT_NUMBER INTEGER NOT NULL , ORIG_AIRPORT CHAR(3), DEPART_TIME TIME, … ) PARTITION BY PRIMARY KEY;

34. Partitioning Options When you wish to partition on a column or columns that are not the primary key, use: PARTITION BY COLUMN (column-name [ , column-name ]*) CREATE TABLE FLIGHTAVAILABILITY ( FLIGHT_ID CHAR(6) NOT NULL , SEGMENT_NUMBER INTEGER NOT NULL , FLIGHT_DATE DATE NOT NULL , ECONOMY_SEATS_TAKEN INTEGER DEFAULT 0, …) PARTITION BY COLUMN (FLIGHT_ID);

35. Partitioning Options You can partition entries based on a range of values of one of the columns: PARTITION BY RANGE (column-name ) ( VALUES BETWEEN value AND value [ , VALUES BETWEEN value AND value ]*) CREATE TABLE FLIGHTAVAILABILITY ( FLIGHT_ID CHAR(6) NOT NULL , SEGMENT_NUMBER INTEGER NOT NULL , FLIGHT_DATE DATE NOT NULL , ECONOMY_SEATS_TAKEN INTEGER DEFAULT 0, …) PARTITION BY RANGE ( economy_seats_taken ) ( VALUES BETWEEN 0 AND 50, VALUES BETWEEN 50 AND 100, VALUES BETWEEN 100 AND 500);

36. Partitioning Options You can explicitly partition entries based on a list of potential values of a column: PARTITION BY LIST ( column-name ) ( VALUES ( value [ , value ]* ) [ , VALUES ( value [ , value ]* ) ]* ) CREATE TABLE Orders (OrderId INT NOT NULL, ItemId INT, NumItems INT, CustomerId INT, OrderDate DATE, Priority INT, Status CHAR(10), CONSTRAINT Pk_Orders PRIMARY KEY (OrderId) CONSTRAINT Fk_Items FOREIGN KEY (ItemId) REFERENCES Items(ItemId)) PARTITION BY LIST ( Status ) ( VALUES ( 'pending', 'returned' ), VALUES ( 'shipped', 'received' ), VALUES ( 'hold' ));

37. Default Partitioning Yes Start Use explicit directives Is partitioning declared? No Is the referenced table partitioned on the foreign key? Yes Colocate with referenced table Yes Are there foreign keys? No If no PARTITION BY clause is specified, GemFire SQLF will automatically partition and colocate tables based on this algorithm. Yes Partition by primary key Is there a primary key? Hashing is performed on the Java implementation of the column’s type. No Yes Partition by the first UNIQUE column Are there UNIQUE columns? No Partition by internally generated row id

38. Demo default partitioned tables, colocation, persistent tables

39. Scaling with Partitioned tables

40. Hash partitioning for linear scaling Key Hashing provides single hop access to its partition But, what if the access is not based on the key … say, joins are involved

41. Hash partitioning only goes so far Consider this query : Select * from flights, flightAvailability where <equijoin flights with flightAvailability> and flightId ='xxx'; If both tables are hash partitioned the join logic will need execution on all nodes where flightavailability data is stored Distributed joins are expensive and inhibit scaling joins across distributed nodes could involve distributed locks and potentially a lot of intermediate data transfer across nodes EquiJOIN of rows across multiple nodes is not supported in SQLFire 1.0

42. Partition aware DB design Designer thinks about how data maps to partitions The main idea is to: minimize excessive data distribution by keeping the most frequently accessed and joined data collocated on partitions Collocate transaction working set on partitions so complex 2-phase commits/paxos commit is eliminated or minimized. Read Pat Helland’s “Life beyond Distributed Transactions” and the Google MegaStore paper

43. Partition aware DB design Turns out OLTP systems lend themselves well to this need Typically it is the number of entities that grows over time and not the size of the entity. Customer count perpetually grows, not the size of the customer info Most often access is very restricted and based on select entities given a FlightID, fetch flightAvailability records given a customerID, add/remove orders, shipment records Identify partition key for “Entity Group” "entity groups": set of entities across several related tables that can all share a single identifier flightIDis shared between the parent and child tables CustomerID shared between customer, order and shipment tables

44. Partition aware DB design Entity groups defined in SQLFire using “colocation” clause Entity group guaranteed to be collocated in presence of failures or rebalance Now, complex queries can be executed without requiring excessive distributed data access

45. Partition Aware DB design STAR schema design is the norm in OLTP design Fact tables (fast changing) are natural partitioning candidates Partition by: FlightID … Availability, history rows colocated with Flights Dimension tables are natural replicated table candidates Replicate Airlines, Countries, Cities on all nodes Dealing with Joins involving M-M relationships Can the one side of the M-M become a replicated table? If not, run the Join logic in a parallel stored procedure to minimize distribution Else, split the query into multiple queries in application

46. Scaling Application logic with Parallel “Data Aware procedures”

47. Procedures Java Stored Procedures may be created according to the SQL Standard CREATE PROCEDURE getOverBookedFlights (IN argument OBJECT, OUT result OBJECT) LANGUAGE JAVA PARAMETER STYLE JAVA READS SQL DATA DYNAMIC RESULT SETS 1 EXTERNAL NAME com.acme.OverBookedFLights; SQLFabric also supports the JDBC type Types.JAVA_OBJECT. A parameter of type JAVA_OBJECT supports an arbitrary Serializable Java object. In this case, the procedure will be executed on the server to which a client is connected (or locally for Peer Clients)

48. Data Aware Procedures CALL [PROCEDURE] procedure_name ( [ expression [, expression ]* ] ) [ WITH RESULT PROCESSOR processor_name ] [ { ON TABLE table_name [ WHERE whereClause ] } | { ON {ALL | SERVER GROUPS (server_group_name [, server_group_name ]*) }} ] Client Fabric Server 1 Fabric Server 2 Parallelize procedure and prune to nodes with required data Extend the procedure call with the following syntax: CALL getOverBookedFlights( <bind arguments> ON TABLE FLIGHTAVAILABILITY WHERE FLIGHTID = <SomeFLIGHTID> ; Hint the data the procedure depends on If table is partitioned by columns in the where clause the procedure execution is pruned to nodes with the data (node with <someFLIGHTID> in this case)

49. Parallelize procedure then aggregate (reduce) CALL [PROCEDURE] procedure_name ( [ expression [, expression ]* ] ) [ WITH RESULT PROCESSOR processor_name] [ { ON TABLE table_name [ WHERE whereClause ] } | { ON {ALL | SERVER GROUPS (server_group_name [, server_group_name ]*) }} ] register a Java Result Processor (optional in some cases): CALL SQLF.CreateResultProcessor( processor_name, processor_class_name); Client Fabric Server 1 Fabric Server 2 Fabric Server 3

50. Consistency model

51. Consistency Model without Transactions Replication within cluster is always eager and synchronous Row updates are always atomic; No need to use transactions FIFO consistency: writes performed by a single thread are seen by all other processes in the order in which they were issued Consistency in Partitioned tables a partitioned table row owned by one member at a point in time all updates are serialized to replicas through owner "Total ordering" at a row level: atomic and isolated Membership changes and consistency Pessimistic concurrency support using ‘Select for update’ Support for referential integrity

52. Distributed Transactions Full support for distributed transactions (Single phase commit) Highly scalable without any centralized coordinator or lock manager We make some important assumptions Most OLTP transactions are small in duration and size W-W conflicts are very rare in practice How does it work? Each data node has a sub-coordinator to track TX state Eagerly acquire local “write” locks on each replica Object owned by a single primary at a point in time Fail fast if lock cannot be obtained Atomic and works with the cluster Failure detection system Isolated until commit Only support local isolation during commit

53. Parallel disk persistence

54. Why is disk latency so high? Challenges Disk seek times is still > 2ms OLTP transactions are small writes Flushing to disk will result in a seek Best rates in 100s per second RDBs and NoSQL try to avoid the problem Append to transaction logs; out-of-band writes to data files But, reads can cause seeks to disk

56. Don’t flush all the way to disk

57. Use OS scheduler to time write

58. Do this on primary + secondary

60. How does it perform? Scale? Scale from 2 to 10 servers (one per host) Scale from 200 to 1200 simulated clients (10 hosts) Single partitioned table: int PK, 40 fields (20 ints, 20 strings)

61. How does it perform? Scale? CPU% remained low per server – about 30% indicating many more clients could be handled

62. Is latency low with scale? Latency decreases with server capacity 50-70% take < 1 millisecond About 90% take less than 2 milliseconds Small percentage of outliers

63. Q & A VMWarevFabricSQLFire BETA will be released in Early June Checkout community.gemstone.com

64. Built using GemFire object data fabric + Derby 52