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Jan 2015 - Cassandra101 Manchester Meetup

This document provides an overview of Apache Cassandra presented by Christopher Batey, a Technical Evangelist for Apache Cassandra. It begins with introductions and background on Batey. It then covers key aspects of Cassandra including distributed databases, Cassandra use cases, replication, fault tolerance, data modeling with Cassandra Query Language (CQL) and the Java driver. Examples are provided around modeling customer event data in Cassandra and querying that data.

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@chbatey Christopher Batey
 Technical Evangelist for Apache Cassandra Apache Cassandra 101
@chbatey Who am I? • Technical Evangelist for Apache Cassandra •Founder of Stubbed Cassandra •Help out Apache Cassandra users • DataStax •Builds enterprise ready version of Apache Cassandra • Previous: Cassandra backed apps at BSkyB
@chbatey Overview • Distributed databases: What and why? • Cassandra use cases • Replication • Fault tolerance • Read and write path • Data modelling • Java Driver
@chbatey Distributed databases
@chbatey It is a big world • Relational - Oracle, PostgreSQL • Graph databases - Neo4J, InfoGrid, Titan • Key value - DynamoDB • Document stores - MongoDB, Couchbase • Columnar aka wide row - Cassandra, HBase
@chbatey Building a web app
@chbatey Running multiple copies of your app
@chbatey Still in one DC?
@chbatey Handling hardware failure
@chbatey Handling hardware failure
@chbatey Master/slave •Master serves all writes •Read from master and optionally slaves
@chbatey Peer-to-Peer • No master • Read/write to any • Consistency?
@chbatey Decisions decisions… CAP theorem Are these really that different?? Relational Database Mongo, Redis Highly Available Databases: Voldermort, Cassandra
@chbatey PAC-ELC • If Partition: Trade off Availability vs Consistency • Else: Trade off Latency vs Consistency • http://dbmsmusings.blogspot.co.uk/2010/04/problems- with-cap-and-yahoos-little.html
@chbatey Cassandra use cases
@chbatey Cassandra for Applications APACHE CASSANDRA
@chbatey Common use cases •Ordered data such as time series -Event stores -Financial transactions -Sensor data e.g IoT
@chbatey Common use cases •Ordered data such as time series -Event stores -Financial transactions -Sensor data e.g IoT •Non functional requirements: -Linear scalability -High throughout durable writes -Multi datacenter including active-active -Analytics without ETL
@chbatey Work loads • High write and/or throughout • Many small queries • Distributed across the world (eventually consistent)
@chbatey Cassandra deep dive
@chbatey Cassandra Cassandra • Distributed masterless database (Dynamo) • Column family data model (Google BigTable)
@chbatey Datacenter and rack aware Europe • Distributed master less database (Dynamo) • Column family data model (Google BigTable) • Multi data centre replication built in from the start USA
@chbatey Cassandra Online • Distributed master less database (Dynamo) • Column family data model (Google BigTable) • Multi data centre replication built in from the start • Analytics with Apache SparkAnalytics
@chbatey Dynamo 101
@chbatey Dynamo 101 • The parts Cassandra took - Consistent hashing - Replication - Strategies for replication - Gossip - Hinted handoff - Anti-entropy repair • And the parts it left behind - Key/Value - Vector clocks
@chbatey Picking the right nodes • You don’t want a full table scan on a 1000 node cluster! • Dynamo to the rescue: Consistent Hashing • Then the replication strategy takes over: - Network topology - Simple
@chbatey Murmer3 Example • Data: • Murmer3 Hash Values: jim age: 36 car: ford gender: M carol age: 37 car: bmw gender: F johnny age: 12 gender: M suzy: age: 10 gender: F Primary Key Murmur3 hash value jim -2245462676723223822 carol 7723358927203680754 johnny -6723372854036780875 suzy 1168604627387940318 Primary Key
@chbatey Company Confidential Murmer3 Example Four node cluster: © 2014 DataStax, All Rights Reserved. Node Murmur3 start range Murmur3 end range A -9223372036854775808 -4611686018427387903 B -4611686018427387904 -1 C 0 4611686018427387903 D 4611686018427387904 9223372036854775807
@chbatey Pictures are better Hash range: -9223372036854775808 to 9223372036854775807 A B C D -4611686018427387903 -1 4611686018427387903 -9223372036854775808 9223372036854775807 -4611686018427387904 0 4611686018427387904 B CD A
@chbatey Murmer3 Example Data is distributed as: Node Start range End range Primary key Hash value A -9223372036854775808 -4611686018427387903 johnny -67233728540367808 75 B -4611686018427387904 -1 jim -22454626767232238 22 C 0 4611686018427387903 suzy 116860462738794031 8 D 4611686018427387904 9223372036854775807 carol 772335892720368075 4
@chbatey Replication
@chbatey Replication strategy • Simple - Give it to the next node in the ring - Don’t use this in production • NetworkTopology - Every Cassandra node knows its DC and Rack - Replicas won’t be put on the same rack unless Replication Factor > # of racks - Unfortunately Cassandra can’t create servers and racks on the fly to fix this :(
@chbatey Replication DC1 DC2 client RF3 RF3 C RC WRITE CL = 1 We have replication!
@chbatey Replication strategy • Simple - Give it to the next node in the ring - Don’t use this in production • NetworkTopology - Every Cassandra node knows its DC and Rack - Replicas won’t be put on the same rack unless Replication Factor > # of racks - Unfortunately Cassandra can’t create servers and racks on the fly to fix this :(
35
@chbatey Tunable Consistency •Data is replicated N times •Every query that you execute you give a consistency -ALL -QUORUM -LOCAL_QUORUM -ONE • Christos Kalantzis Eventual Consistency != Hopeful Consistency: http:// youtu.be/A6qzx_HE3EU?list=PLqcm6qE9lgKJzVvwHprow9h7KMpb5hcUU
@chbatey Handling hardware failure Async Replication
@chbatey Load balancing •Data centre aware policy •Token aware policy •Latency aware policy •Whitelist policy APP APP Async Replication DC1 DC2
@chbatey Handling hardware failure Async Replication
@chbatey Handling hardware failure Async Replication
@chbatey Handling hardware failure Async Replication
@chbatey But what happens when they come back? • Hinted handoff to the rescue • Coordinators keep writes for downed nodes for a configurable amount of time, default 3 hours • Longer than that run a repair
@chbatey Anti entropy repair • Not exciting but mandatory :) • New in 2.1 - incremental repair <— awesome
@chbatey Don’t forget to be social • Each node talks to a few of its other and shares information Did you hear node 1 was down???
@chbatey Scaling shouldn’t be hard • Throw more nodes at a cluster • Bootstrapping + joining the ring • For large data sets this can take some time
@chbatey Data modelling
@chbatey You must denormalise
@chbatey Your model should look like your queries
@chbatey CQL •Cassandra Query Language -SQL like query language •Keyspace – analogous to a schema - The keyspace determines the RF (replication factor) •Table – looks like a SQL Table CREATE TABLE scores ( name text, score int, date timestamp, PRIMARY KEY (name, score) ); INSERT INTO scores (name, score, date) VALUES ('bob', 42, '2012-06-24'); INSERT INTO scores (name, score, date) VALUES ('bob', 47, '2012-06-25'); SELECT date, score FROM scores WHERE name='bob' AND score >= 40;
@chbatey Lots of types
@chbatey UUID • Universal Unique ID - 128 bit number represented in character form e.g. 99051fe9-6a9c-46c2-b949-38ef78858dd0 • Easily generated on the client - Version 1 has a timestamp component (TIMEUUID) - Version 4 has no timestamp component
@chbatey Company Confidential TIMEUUID TIMEUUID data type supports Version 1 UUIDs Generated using time (60 bits), a clock sequence number (14 bits), and MAC address (48 bits) – CQL function ‘now()’ generates a new TIMEUUID Time can be extracted from TIMEUUID – CQL function dateOf() extracts the timestamp as a date TIMEUUID values in clustering columns or in column names are ordered based on time – DESC order on TIMEUUID lists most recent data first © 2014 DataStax, All Rights Reserved.
@chbatey Collections CREATE TABLE videos ( videoid uuid, userid uuid, name varchar, description varchar, location text, location_type int, preview_thumbnails map<text,text>, tags set<varchar>, added_date timestamp, PRIMARY KEY (videoid) );
@chbatey Data Model - User Defined Types • Complex data in one place • No multi-gets (multi-partitions) • Nesting! CREATE TYPE address ( street text, city text, zip_code int, country text, cross_streets set<text> );
Data Model - Updated • We can embed video_metadata in videos CREATE TYPE video_metadata ( height int, width int, video_bit_rate set<text>, encoding text ); CREATE TABLE videos ( videoid uuid, userid uuid, name varchar, description varchar, location text, location_type int, preview_thumbnails map<text,text>, tags set<varchar>, metadata set <frozen<video_metadata>>, added_date timestamp, PRIMARY KEY (videoid) );
Data Model - Storing JSON { "productId": 2, "name": "Kitchen Table", "price": 249.99, "description" : "Rectangular table with oak finish", "dimensions": { "units": "inches", "length": 50.0, "width": 66.0, "height": 32 }, "categories": { { "category" : "Home Furnishings" { "catalogPage": 45, "url": "/home/furnishings" }, { "category" : "Kitchen Furnishings" { "catalogPage": 108, "url": "/kitchen/furnishings" } } } CREATE TYPE dimensions ( units text, length float, width float, height float ); CREATE TYPE category ( catalogPage int, url text ); CREATE TABLE product ( productId int, name text, price float, description text, dimensions frozen <dimensions>, categories map <text, frozen <category>>, PRIMARY KEY (productId) );
@chbatey Tuple type • Type to represent a group • Up to 256 different elements CREATE TABLE tuple_table ( id int PRIMARY KEY, three_tuple frozen <tuple<int, text, float>>, four_tuple frozen <tuple<int, text, float, inet>>, five_tuple frozen <tuple<int, text, float, inet, ascii>> );
@chbatey Counters • Old has been around since .8 • Commit log replay changes counters • Repair can change a counter
@chbatey Time-to-Live (TTL) TTL a row: INSERT INTO users (id, first, last) VALUES (‘abc123’, ‘catherine’, ‘cachart’) USING TTL 3600; // Expires data in one hour
 TTL a column: UPDATE users USING TTL 30 SET last = ‘miller’ WHERE id = ‘abc123’ – TTL in seconds – Can also set default TTL at a table level – Expired columns/values automatically deleted – With no TTL specified, columns/values never expire – TTL is useful for automatic deletion – Re-inserting the same row before it expires will overwrite TTL
@chbatey DevCenter
@chbatey Example Time: Customer event store
@chbatey An example: Customer event store • Customer event • customer_id - ChrisBatey • staff_id - Charlie • store_type Website, PhoneApp, Phone, Retail • event_type - login, logout, add_to_basket, remove_from_basket, buy_item • time • tags
@chbatey Requirements • Get all events • Get all events for a particular customer • As above for a time slice
Modelling in Cassandra CREATE TABLE customer_events( customer_id text, staff_id text, time timeuuid, store_type text, event_type text, tags map<text, text>, PRIMARY KEY ((customer_id), time)); Partition Key Clustering Column(s)
How it is stored on disk customer _id time event_type store_type tags charles 2014-11-18 16:52:04 basket_add online {'item': 'coffee'} charles 2014-11-18 16:53:00 basket_add online {'item': ‘wine'} charles 2014-11-18 16:53:09 logout online {} chbatey 2014-11-18 16:52:21 login online {} chbatey 2014-11-18 16:53:21 basket_add online {'item': 'coffee'} chbatey 2014-11-18 16:54:00 basket_add online {'item': 'cheese'} charles event_type basket_add staff_id n/a store_type online tags:item coffee event_type basket_add staff_id n/a store_type online tags:item wine event_type logout staff_id n/a store_type online chbatey event_type login staff_id n/a store_type online event_type basket_add staff_id n/a store_type online tags:item coffee event_type basket_add staff_id n/a store_type online tags:item cheese
@chbatey Drivers
@chbatey Languages • DataStax (open source) - C#, Java, C++, Python, Node, Ruby - Very similar programming API • Other open source - Go - Clojure - Erlang - Haskell - Many more Java/Python drivers - Perl
@chbatey DataStax Java Driver • Open source
@chbatey Get all the events public List<CustomerEvent> getAllCustomerEvents() {
 return session.execute("select * from customers.customer_events")
 .all().stream()
 .map(mapCustomerEvent())
 .collect(Collectors.toList());
 } private Function<Row, CustomerEvent> mapCustomerEvent() {
 return row -> new CustomerEvent(
 row.getString("customer_id"),
 row.getUUID("time"),
 row.getString("staff_id"),
 row.getString("store_type"),
 row.getString("event_type"),
 row.getMap("tags", String.class, String.class));
 }
@chbatey All events for a particular customer private PreparedStatement getEventsForCustomer;
 
 @PostConstruct
 public void prepareSatements() {
 getEventsForCustomer = session.prepare("select * from customers.customer_events where customer_id = ?");
 }
 
 public List<CustomerEvent> getCustomerEvents(String customerId) {
 BoundStatement boundStatement = getEventsForCustomer.bind(customerId);
 return session.execute(boundStatement) .all().stream()
 .map(mapCustomerEvent())
 .collect(Collectors.toList());
 }
@chbatey Customer events for a time slice public List<CustomerEvent> getCustomerEventsForTime(String customerId, long startTime, long endTime) { 
 Select.Where getCustomers = QueryBuilder.select()
 .all()
 .from("customers", "customer_events")
 .where(eq("customer_id", customerId))
 .and(gt("time", UUIDs.startOf(startTime)))
 .and(lt("time", UUIDs.endOf(endTime)));
 
 
 return session.execute(getCustomers).all().stream()
 .map(mapCustomerEvent())
 .collect(Collectors.toList());
 }
@chbatey Mapping API @Table(keyspace = "customers", name = "customer_events")
 public class CustomerEvent {
 @PartitionKey
 @Column(name = "customer_id")
 private String customerId;
 
 @ClusteringColumn
 private UUID time;
 
 @Column(name = "staff_id")
 private String staffId;
 
 @Column(name = "store_type")
 private String storeType;
 
 @Column(name = "event_type")
 private String eventType;
 
 private Map<String, String> tags; // ctr / getters etc }

@chbatey Mapping API @Accessor
 public interface CustomerEventDao {
 @Query("select * from customers.customer_events where customer_id = :customerId")
 Result<CustomerEvent> getCustomerEvents(String customerId);
 
 @Query("select * from customers.customer_events")
 Result<CustomerEvent> getAllCustomerEvents();
 
 @Query("select * from customers.customer_events where customer_id = :customerId and time > minTimeuuid(:startTime) and time < maxTimeuuid(:endTime)")
 Result<CustomerEvent> getCustomerEventsForTime(String customerId, long startTime, long endTime);
 } 
 @Bean
 public CustomerEventDao customerEventDao() {
 MappingManager mappingManager = new MappingManager(session);
 return mappingManager.createAccessor(CustomerEventDao.class);
 }
@chbatey Adding some type safety public enum StoreType {
 ONLINE, RETAIL, FRANCHISE, MOBILE
 } @Table(keyspace = "customers", name = "customer_events")
 public class CustomerEvent {
 @PartitionKey
 @Column(name = "customer_id")
 private String customerId;
 
 @ClusteringColumn()
 private UUID time;
 
 @Column(name = "staff_id")
 private String staffId;
 
 @Column(name = "store_type")
 @Enumerated(EnumType.STRING) // could be EnumType.ORDINAL
 private StoreType storeType;

@chbatey User defined types create TYPE store (name text, type text, postcode text) ;
 
 
 CREATE TABLE customer_events_type( customer_id text, staff_id text, time timeuuid, store frozen<store>, event_type text, tags map<text, text>, PRIMARY KEY ((customer_id), time));

@chbatey Mapping user defined types @UDT(keyspace = "customers", name = "store")
 public class Store {
 private String name;
 private StoreType type;
 private String postcode; // getters etc } @Table(keyspace = "customers", name = "customer_events_type")
 public class CustomerEventType {
 @PartitionKey
 @Column(name = "customer_id")
 private String customerId;
 
 @ClusteringColumn()
 private UUID time;
 
 @Column(name = "staff_id")
 private String staffId;
 
 @Frozen
 private Store store;
 
 @Column(name = "event_type")
 private String eventType;
 
 private Map<String, String> tags;

@chbatey Mapping user defined types @UDT(keyspace = "customers", name = "store")
 public class Store {
 private String name;
 private StoreType type;
 private String postcode; // getters etc } @Table(keyspace = "customers", name = "customer_events_type")
 public class CustomerEventType {
 @PartitionKey
 @Column(name = "customer_id")
 private String customerId;
 
 @ClusteringColumn()
 private UUID time;
 
 @Column(name = "staff_id")
 private String staffId;
 
 @Frozen
 private Store store;
 
 @Column(name = "event_type")
 private String eventType;
 
 private Map<String, String> tags;
 @Query("select * from customers.customer_events_type")
 Result<CustomerEventType> getAllCustomerEventsWithStoreType();
@chbatey ©2014 DataStax. Do not distribute without consent. Query Tracing Connected to cluster: xerxes Simplex keyspace and schema created. Host (queried): /127.0.0.1 Host (tried): /127.0.0.1 Trace id: 96ac9400-a3a5-11e2-96a9-4db56cdc5fe7 activity | timestamp | source | source_elapsed ---------------------------------------+--------------+------------+-------------- Parsing statement | 12:17:16.736 | /127.0.0.1 | 28 Peparing statement | 12:17:16.736 | /127.0.0.1 | 199 Determining replicas for mutation | 12:17:16.736 | /127.0.0.1 | 348 Sending message to /127.0.0.3 | 12:17:16.736 | /127.0.0.1 | 788 Sending message to /127.0.0.2 | 12:17:16.736 | /127.0.0.1 | 805 Acquiring switchLock read lock | 12:17:16.736 | /127.0.0.1 | 828 Appending to commitlog | 12:17:16.736 | /127.0.0.1 | 848 Adding to songs memtable | 12:17:16.736 | /127.0.0.1 | 900 Message received from /127.0.0.1 | 12:17:16.737 | /127.0.0.2 | 34 Message received from /127.0.0.1 | 12:17:16.737 | /127.0.0.3 | 25 Acquiring switchLock read lock | 12:17:16.737 | /127.0.0.2 | 672 Acquiring switchLock read lock | 12:17:16.737 | /127.0.0.3 | 525
@chbatey Other features
@chbatey Light weight transactions • Often referred to as compare and set (CAS) INSERT INTO USERS (login, email, name, login_count) values ('jbellis', 'jbellis@datastax.com', 'Jonathan Ellis', 1) IF NOT EXISTS
@chbatey Batch Statements BEGIN BATCH INSERT INTO users (userID, password, name) VALUES ('user2', 'ch@ngem3b', 'second user') UPDATE users SET password = 'ps22dhds' WHERE userID = 'user2' INSERT INTO users (userID, password) VALUES ('user3', 'ch@ngem3c') DELETE name FROM users WHERE userID = 'user2’ APPLY BATCH;
 BATCH statement combines multiple INSERT, UPDATE, and DELETE statements into a single logical operation 
 Atomic operation If any statement in the batch succeeds, all will No batch isolation Other “transactions” can read and write data being affected by a partially executed batch
@chbatey Company Confidential Batch Statements with LWT BEGIN BATCH UPDATE foo SET z = 1 WHERE x = 'a' AND y = 1; UPDATE foo SET z = 2 WHERE x = 'a' AND y = 2 IF t = 4; APPLY BATCH; Allows you to group multiple conditional updates in a batch as long as all those updates apply to the same partition
 © 2014 DataStax, All Rights Reserved.
@chbatey Summary • Cassandra is a shared nothing masterless datastore • Availability a.k.a up time is king • Biggest hurdle is learning to model differently • Modern drivers make it easy to work with
@chbatey Thanks for listening • Follow me on twitter @chbatey • Cassandra + Fault tolerance posts a plenty: • http://christopher-batey.blogspot.co.uk/ • Cassandra resources: http://planetcassandra.org/

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Jan 2015 - Cassandra101 Manchester Meetup

  • 1.
    @chbatey Christopher Batey
 Technical Evangelistfor Apache Cassandra Apache Cassandra 101
  • 2.
    @chbatey Who am I? •Technical Evangelist for Apache Cassandra •Founder of Stubbed Cassandra •Help out Apache Cassandra users • DataStax •Builds enterprise ready version of Apache Cassandra • Previous: Cassandra backed apps at BSkyB
  • 3.
    @chbatey Overview • Distributed databases:What and why? • Cassandra use cases • Replication • Fault tolerance • Read and write path • Data modelling • Java Driver
  • 4.
  • 5.
    @chbatey It is abig world • Relational - Oracle, PostgreSQL • Graph databases - Neo4J, InfoGrid, Titan • Key value - DynamoDB • Document stores - MongoDB, Couchbase • Columnar aka wide row - Cassandra, HBase
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
    @chbatey Master/slave •Master serves allwrites •Read from master and optionally slaves
  • 12.
    @chbatey Peer-to-Peer • No master •Read/write to any • Consistency?
  • 13.
    @chbatey Decisions decisions… CAPtheorem Are these really that different?? Relational Database Mongo, Redis Highly Available Databases: Voldermort, Cassandra
  • 14.
    @chbatey PAC-ELC • If Partition:Trade off Availability vs Consistency • Else: Trade off Latency vs Consistency • http://dbmsmusings.blogspot.co.uk/2010/04/problems- with-cap-and-yahoos-little.html
  • 15.
  • 16.
  • 17.
    @chbatey Common use cases •Ordereddata such as time series -Event stores -Financial transactions -Sensor data e.g IoT
  • 18.
    @chbatey Common use cases •Ordereddata such as time series -Event stores -Financial transactions -Sensor data e.g IoT •Non functional requirements: -Linear scalability -High throughout durable writes -Multi datacenter including active-active -Analytics without ETL
  • 19.
    @chbatey Work loads • Highwrite and/or throughout • Many small queries • Distributed across the world (eventually consistent)
  • 20.
  • 21.
    @chbatey Cassandra Cassandra • Distributed masterless database(Dynamo) • Column family data model (Google BigTable)
  • 22.
    @chbatey Datacenter and rackaware Europe • Distributed master less database (Dynamo) • Column family data model (Google BigTable) • Multi data centre replication built in from the start USA
  • 23.
    @chbatey Cassandra Online • Distributed masterless database (Dynamo) • Column family data model (Google BigTable) • Multi data centre replication built in from the start • Analytics with Apache SparkAnalytics
  • 24.
  • 25.
    @chbatey Dynamo 101 • Theparts Cassandra took - Consistent hashing - Replication - Strategies for replication - Gossip - Hinted handoff - Anti-entropy repair • And the parts it left behind - Key/Value - Vector clocks
  • 26.
    @chbatey Picking the rightnodes • You don’t want a full table scan on a 1000 node cluster! • Dynamo to the rescue: Consistent Hashing • Then the replication strategy takes over: - Network topology - Simple
  • 27.
    @chbatey Murmer3 Example • Data: •Murmer3 Hash Values: jim age: 36 car: ford gender: M carol age: 37 car: bmw gender: F johnny age: 12 gender: M suzy: age: 10 gender: F Primary Key Murmur3 hash value jim -2245462676723223822 carol 7723358927203680754 johnny -6723372854036780875 suzy 1168604627387940318 Primary Key
  • 28.
    @chbatey Company Confidential Murmer3 Example Fournode cluster: © 2014 DataStax, All Rights Reserved. Node Murmur3 start range Murmur3 end range A -9223372036854775808 -4611686018427387903 B -4611686018427387904 -1 C 0 4611686018427387903 D 4611686018427387904 9223372036854775807
  • 29.
    @chbatey Pictures are better Hashrange: -9223372036854775808 to 9223372036854775807 A B C D -4611686018427387903 -1 4611686018427387903 -9223372036854775808 9223372036854775807 -4611686018427387904 0 4611686018427387904 B CD A
  • 30.
    @chbatey Murmer3 Example Data isdistributed as: Node Start range End range Primary key Hash value A -9223372036854775808 -4611686018427387903 johnny -67233728540367808 75 B -4611686018427387904 -1 jim -22454626767232238 22 C 0 4611686018427387903 suzy 116860462738794031 8 D 4611686018427387904 9223372036854775807 carol 772335892720368075 4
  • 31.
  • 32.
    @chbatey Replication strategy • Simple -Give it to the next node in the ring - Don’t use this in production • NetworkTopology - Every Cassandra node knows its DC and Rack - Replicas won’t be put on the same rack unless Replication Factor > # of racks - Unfortunately Cassandra can’t create servers and racks on the fly to fix this :(
  • 33.
  • 34.
    @chbatey Replication strategy • Simple -Give it to the next node in the ring - Don’t use this in production • NetworkTopology - Every Cassandra node knows its DC and Rack - Replicas won’t be put on the same rack unless Replication Factor > # of racks - Unfortunately Cassandra can’t create servers and racks on the fly to fix this :(
  • 35.
  • 36.
    @chbatey Tunable Consistency •Data isreplicated N times •Every query that you execute you give a consistency -ALL -QUORUM -LOCAL_QUORUM -ONE • Christos Kalantzis Eventual Consistency != Hopeful Consistency: http:// youtu.be/A6qzx_HE3EU?list=PLqcm6qE9lgKJzVvwHprow9h7KMpb5hcUU
  • 37.
  • 38.
    @chbatey Load balancing •Data centreaware policy •Token aware policy •Latency aware policy •Whitelist policy APP APP Async Replication DC1 DC2
  • 39.
  • 40.
  • 41.
  • 42.
    @chbatey But what happenswhen they come back? • Hinted handoff to the rescue • Coordinators keep writes for downed nodes for a configurable amount of time, default 3 hours • Longer than that run a repair
  • 43.
    @chbatey Anti entropy repair •Not exciting but mandatory :) • New in 2.1 - incremental repair <— awesome
  • 44.
    @chbatey Don’t forget tobe social • Each node talks to a few of its other and shares information Did you hear node 1 was down???
  • 45.
    @chbatey Scaling shouldn’t behard • Throw more nodes at a cluster • Bootstrapping + joining the ring • For large data sets this can take some time
  • 46.
  • 47.
  • 48.
    @chbatey Your model shouldlook like your queries
  • 49.
    @chbatey CQL •Cassandra Query Language -SQLlike query language •Keyspace – analogous to a schema - The keyspace determines the RF (replication factor) •Table – looks like a SQL Table CREATE TABLE scores ( name text, score int, date timestamp, PRIMARY KEY (name, score) ); INSERT INTO scores (name, score, date) VALUES ('bob', 42, '2012-06-24'); INSERT INTO scores (name, score, date) VALUES ('bob', 47, '2012-06-25'); SELECT date, score FROM scores WHERE name='bob' AND score >= 40;
  • 50.
  • 51.
    @chbatey UUID • Universal UniqueID - 128 bit number represented in character form e.g. 99051fe9-6a9c-46c2-b949-38ef78858dd0 • Easily generated on the client - Version 1 has a timestamp component (TIMEUUID) - Version 4 has no timestamp component
  • 52.
    @chbatey Company Confidential TIMEUUID TIMEUUID datatype supports Version 1 UUIDs Generated using time (60 bits), a clock sequence number (14 bits), and MAC address (48 bits) – CQL function ‘now()’ generates a new TIMEUUID Time can be extracted from TIMEUUID – CQL function dateOf() extracts the timestamp as a date TIMEUUID values in clustering columns or in column names are ordered based on time – DESC order on TIMEUUID lists most recent data first © 2014 DataStax, All Rights Reserved.
  • 53.
    @chbatey Collections CREATE TABLE videos( videoid uuid, userid uuid, name varchar, description varchar, location text, location_type int, preview_thumbnails map<text,text>, tags set<varchar>, added_date timestamp, PRIMARY KEY (videoid) );
  • 54.
    @chbatey Data Model -User Defined Types • Complex data in one place • No multi-gets (multi-partitions) • Nesting! CREATE TYPE address ( street text, city text, zip_code int, country text, cross_streets set<text> );
  • 55.
    Data Model -Updated • We can embed video_metadata in videos CREATE TYPE video_metadata ( height int, width int, video_bit_rate set<text>, encoding text ); CREATE TABLE videos ( videoid uuid, userid uuid, name varchar, description varchar, location text, location_type int, preview_thumbnails map<text,text>, tags set<varchar>, metadata set <frozen<video_metadata>>, added_date timestamp, PRIMARY KEY (videoid) );
  • 56.
    Data Model -Storing JSON { "productId": 2, "name": "Kitchen Table", "price": 249.99, "description" : "Rectangular table with oak finish", "dimensions": { "units": "inches", "length": 50.0, "width": 66.0, "height": 32 }, "categories": { { "category" : "Home Furnishings" { "catalogPage": 45, "url": "/home/furnishings" }, { "category" : "Kitchen Furnishings" { "catalogPage": 108, "url": "/kitchen/furnishings" } } } CREATE TYPE dimensions ( units text, length float, width float, height float ); CREATE TYPE category ( catalogPage int, url text ); CREATE TABLE product ( productId int, name text, price float, description text, dimensions frozen <dimensions>, categories map <text, frozen <category>>, PRIMARY KEY (productId) );
  • 57.
    @chbatey Tuple type • Typeto represent a group • Up to 256 different elements CREATE TABLE tuple_table ( id int PRIMARY KEY, three_tuple frozen <tuple<int, text, float>>, four_tuple frozen <tuple<int, text, float, inet>>, five_tuple frozen <tuple<int, text, float, inet, ascii>> );
  • 58.
    @chbatey Counters • Old hasbeen around since .8 • Commit log replay changes counters • Repair can change a counter
  • 59.
    @chbatey Time-to-Live (TTL) TTL arow: INSERT INTO users (id, first, last) VALUES (‘abc123’, ‘catherine’, ‘cachart’) USING TTL 3600; // Expires data in one hour
 TTL a column: UPDATE users USING TTL 30 SET last = ‘miller’ WHERE id = ‘abc123’ – TTL in seconds – Can also set default TTL at a table level – Expired columns/values automatically deleted – With no TTL specified, columns/values never expire – TTL is useful for automatic deletion – Re-inserting the same row before it expires will overwrite TTL
  • 60.
  • 61.
  • 62.
    @chbatey An example: Customerevent store • Customer event • customer_id - ChrisBatey • staff_id - Charlie • store_type Website, PhoneApp, Phone, Retail • event_type - login, logout, add_to_basket, remove_from_basket, buy_item • time • tags
  • 63.
    @chbatey Requirements • Get allevents • Get all events for a particular customer • As above for a time slice
  • 64.
    Modelling in Cassandra CREATETABLE customer_events( customer_id text, staff_id text, time timeuuid, store_type text, event_type text, tags map<text, text>, PRIMARY KEY ((customer_id), time)); Partition Key Clustering Column(s)
  • 65.
    How it isstored on disk customer _id time event_type store_type tags charles 2014-11-18 16:52:04 basket_add online {'item': 'coffee'} charles 2014-11-18 16:53:00 basket_add online {'item': ‘wine'} charles 2014-11-18 16:53:09 logout online {} chbatey 2014-11-18 16:52:21 login online {} chbatey 2014-11-18 16:53:21 basket_add online {'item': 'coffee'} chbatey 2014-11-18 16:54:00 basket_add online {'item': 'cheese'} charles event_type basket_add staff_id n/a store_type online tags:item coffee event_type basket_add staff_id n/a store_type online tags:item wine event_type logout staff_id n/a store_type online chbatey event_type login staff_id n/a store_type online event_type basket_add staff_id n/a store_type online tags:item coffee event_type basket_add staff_id n/a store_type online tags:item cheese
  • 66.
  • 67.
    @chbatey Languages • DataStax (opensource) - C#, Java, C++, Python, Node, Ruby - Very similar programming API • Other open source - Go - Clojure - Erlang - Haskell - Many more Java/Python drivers - Perl
  • 68.
  • 69.
    @chbatey Get all theevents public List<CustomerEvent> getAllCustomerEvents() {
 return session.execute("select * from customers.customer_events")
 .all().stream()
 .map(mapCustomerEvent())
 .collect(Collectors.toList());
 } private Function<Row, CustomerEvent> mapCustomerEvent() {
 return row -> new CustomerEvent(
 row.getString("customer_id"),
 row.getUUID("time"),
 row.getString("staff_id"),
 row.getString("store_type"),
 row.getString("event_type"),
 row.getMap("tags", String.class, String.class));
 }
  • 70.
    @chbatey All events fora particular customer private PreparedStatement getEventsForCustomer;
 
 @PostConstruct
 public void prepareSatements() {
 getEventsForCustomer = session.prepare("select * from customers.customer_events where customer_id = ?");
 }
 
 public List<CustomerEvent> getCustomerEvents(String customerId) {
 BoundStatement boundStatement = getEventsForCustomer.bind(customerId);
 return session.execute(boundStatement) .all().stream()
 .map(mapCustomerEvent())
 .collect(Collectors.toList());
 }
  • 71.
    @chbatey Customer events fora time slice public List<CustomerEvent> getCustomerEventsForTime(String customerId, long startTime, long endTime) { 
 Select.Where getCustomers = QueryBuilder.select()
 .all()
 .from("customers", "customer_events")
 .where(eq("customer_id", customerId))
 .and(gt("time", UUIDs.startOf(startTime)))
 .and(lt("time", UUIDs.endOf(endTime)));
 
 
 return session.execute(getCustomers).all().stream()
 .map(mapCustomerEvent())
 .collect(Collectors.toList());
 }
  • 72.
    @chbatey Mapping API @Table(keyspace ="customers", name = "customer_events")
 public class CustomerEvent {
 @PartitionKey
 @Column(name = "customer_id")
 private String customerId;
 
 @ClusteringColumn
 private UUID time;
 
 @Column(name = "staff_id")
 private String staffId;
 
 @Column(name = "store_type")
 private String storeType;
 
 @Column(name = "event_type")
 private String eventType;
 
 private Map<String, String> tags; // ctr / getters etc }

  • 73.
    @chbatey Mapping API @Accessor
 public interfaceCustomerEventDao {
 @Query("select * from customers.customer_events where customer_id = :customerId")
 Result<CustomerEvent> getCustomerEvents(String customerId);
 
 @Query("select * from customers.customer_events")
 Result<CustomerEvent> getAllCustomerEvents();
 
 @Query("select * from customers.customer_events where customer_id = :customerId and time > minTimeuuid(:startTime) and time < maxTimeuuid(:endTime)")
 Result<CustomerEvent> getCustomerEventsForTime(String customerId, long startTime, long endTime);
 } 
 @Bean
 public CustomerEventDao customerEventDao() {
 MappingManager mappingManager = new MappingManager(session);
 return mappingManager.createAccessor(CustomerEventDao.class);
 }
  • 74.
    @chbatey Adding some typesafety public enum StoreType {
 ONLINE, RETAIL, FRANCHISE, MOBILE
 } @Table(keyspace = "customers", name = "customer_events")
 public class CustomerEvent {
 @PartitionKey
 @Column(name = "customer_id")
 private String customerId;
 
 @ClusteringColumn()
 private UUID time;
 
 @Column(name = "staff_id")
 private String staffId;
 
 @Column(name = "store_type")
 @Enumerated(EnumType.STRING) // could be EnumType.ORDINAL
 private StoreType storeType;

  • 75.
    @chbatey User defined types createTYPE store (name text, type text, postcode text) ;
 
 
 CREATE TABLE customer_events_type( customer_id text, staff_id text, time timeuuid, store frozen<store>, event_type text, tags map<text, text>, PRIMARY KEY ((customer_id), time));

  • 76.
    @chbatey Mapping user definedtypes @UDT(keyspace = "customers", name = "store")
 public class Store {
 private String name;
 private StoreType type;
 private String postcode; // getters etc } @Table(keyspace = "customers", name = "customer_events_type")
 public class CustomerEventType {
 @PartitionKey
 @Column(name = "customer_id")
 private String customerId;
 
 @ClusteringColumn()
 private UUID time;
 
 @Column(name = "staff_id")
 private String staffId;
 
 @Frozen
 private Store store;
 
 @Column(name = "event_type")
 private String eventType;
 
 private Map<String, String> tags;

  • 77.
    @chbatey Mapping user definedtypes @UDT(keyspace = "customers", name = "store")
 public class Store {
 private String name;
 private StoreType type;
 private String postcode; // getters etc } @Table(keyspace = "customers", name = "customer_events_type")
 public class CustomerEventType {
 @PartitionKey
 @Column(name = "customer_id")
 private String customerId;
 
 @ClusteringColumn()
 private UUID time;
 
 @Column(name = "staff_id")
 private String staffId;
 
 @Frozen
 private Store store;
 
 @Column(name = "event_type")
 private String eventType;
 
 private Map<String, String> tags;
 @Query("select * from customers.customer_events_type")
 Result<CustomerEventType> getAllCustomerEventsWithStoreType();
  • 78.
    @chbatey ©2014 DataStax. Donot distribute without consent. Query Tracing Connected to cluster: xerxes Simplex keyspace and schema created. Host (queried): /127.0.0.1 Host (tried): /127.0.0.1 Trace id: 96ac9400-a3a5-11e2-96a9-4db56cdc5fe7 activity | timestamp | source | source_elapsed ---------------------------------------+--------------+------------+-------------- Parsing statement | 12:17:16.736 | /127.0.0.1 | 28 Peparing statement | 12:17:16.736 | /127.0.0.1 | 199 Determining replicas for mutation | 12:17:16.736 | /127.0.0.1 | 348 Sending message to /127.0.0.3 | 12:17:16.736 | /127.0.0.1 | 788 Sending message to /127.0.0.2 | 12:17:16.736 | /127.0.0.1 | 805 Acquiring switchLock read lock | 12:17:16.736 | /127.0.0.1 | 828 Appending to commitlog | 12:17:16.736 | /127.0.0.1 | 848 Adding to songs memtable | 12:17:16.736 | /127.0.0.1 | 900 Message received from /127.0.0.1 | 12:17:16.737 | /127.0.0.2 | 34 Message received from /127.0.0.1 | 12:17:16.737 | /127.0.0.3 | 25 Acquiring switchLock read lock | 12:17:16.737 | /127.0.0.2 | 672 Acquiring switchLock read lock | 12:17:16.737 | /127.0.0.3 | 525
  • 79.
  • 80.
    @chbatey Light weight transactions •Often referred to as compare and set (CAS) INSERT INTO USERS (login, email, name, login_count) values ('jbellis', 'jbellis@datastax.com', 'Jonathan Ellis', 1) IF NOT EXISTS
  • 81.
    @chbatey Batch Statements BEGIN BATCH INSERTINTO users (userID, password, name) VALUES ('user2', 'ch@ngem3b', 'second user') UPDATE users SET password = 'ps22dhds' WHERE userID = 'user2' INSERT INTO users (userID, password) VALUES ('user3', 'ch@ngem3c') DELETE name FROM users WHERE userID = 'user2’ APPLY BATCH;
 BATCH statement combines multiple INSERT, UPDATE, and DELETE statements into a single logical operation 
 Atomic operation If any statement in the batch succeeds, all will No batch isolation Other “transactions” can read and write data being affected by a partially executed batch
  • 82.
    @chbatey Company Confidential Batch Statementswith LWT BEGIN BATCH UPDATE foo SET z = 1 WHERE x = 'a' AND y = 1; UPDATE foo SET z = 2 WHERE x = 'a' AND y = 2 IF t = 4; APPLY BATCH; Allows you to group multiple conditional updates in a batch as long as all those updates apply to the same partition
 © 2014 DataStax, All Rights Reserved.
  • 83.
    @chbatey Summary • Cassandra isa shared nothing masterless datastore • Availability a.k.a up time is king • Biggest hurdle is learning to model differently • Modern drivers make it easy to work with
  • 84.
    @chbatey Thanks for listening •Follow me on twitter @chbatey • Cassandra + Fault tolerance posts a plenty: • http://christopher-batey.blogspot.co.uk/ • Cassandra resources: http://planetcassandra.org/