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Cassandra Summit 2014: Understanding CQL3 Inside and Out

The document discusses the introduction of CQL (Cassandra Query Language) to address usability challenges in Cassandra, emphasizing its familiarity with SQL-like syntax. It outlines the benefits of Cassandra as a distributed, scalable data store while highlighting issues such as schema flexibility that complicate data comprehension and interaction. CQL aims to simplify interactions by reintroducing schema concepts, making data models easier to understand and manage.

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The CQL3/Cassandra Mapping John Berryman OpenSource Connections OpenSource Connections
Outline • What Problem does CQL Solve? • The Cassandra Data Model • Pain Points of “Old” Cassandra • Introducing CQL • Understanding the CQL/Cassandra Mapping • CQL for Sets, Lists, and Maps • Putting it All Together OpenSource Connections
What Problem does CQL Solve? • The Awesomeness that is Cassandra: o Distributed columnar data store o No single point of failure o Optimized for availability (though “Tunably” consistent) o Optimized for writes o Easily maintainable o Almost infinitely scalable . OpenSource Connections
What Problem does CQL Solve? • The Awesomeness that is Cassandra: o Distributed columnar data store o No single point of failure o Optimized for availability (though “Tunably” consistent) o Optimized for writes o Easily maintainable o Almost infinitely scalable • Cassandra’s usability challenges o NoSQL – “Where are my JOINS? No Schema? De-normalize!?” o BigTable – “Tables with millions of columns!?” . OpenSource Connections
What Problem does CQL Solve? • The Awesomeness that is Cassandra: o Distributed columnar data store o No single point of failure o Optimized for availability (though “Tunably” consistent) o Optimized for writes o Easily maintainable o Almost infinitely scalable • Cassandra’s usability challenges o NoSQL – “Where are my JOINS? No Schema? De-normalize!?” o BigTable – “Tables with millions of columns!?” • CQL saves the day! o A best-practices interface to Cassandra o Uses familiar SQL-like language OpenSource Connections
C* Data Model OpenSource Connections Keyspace
C* Data Model Keyspace Column Family Column Family OpenSource Connections
C* Data Model Keyspace Column Family Column Family OpenSource Connections
C* Data Model Keyspace Column Family Column Family OpenSource Connections
C* Data Model OpenSource Connections Row Key
C* Data Model OpenSource Connections Row Key Column Column Name Column Value (or Tombstone) Timestamp Time-to-live
C* Data Model ● Row Key, Column Name, Column OpenSource Connections Value have types ● Column Name has comparator ● RowKey has partitioner ● Rows can have any number of columns - even in same column family ● Rows can have many columns ● Column Values can be omitted ● Time-to-live is useful! ● Tombstones Row Key Column Column Name Column Value (or Tombstone) Timestamp Time-to-live
C* Data Model: Writes SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache ● Insert into MemTable ● Dump to CommitLog ● No read ● Very Fast! ● Blocks on CPU before O/I! Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter
C* Data Model: Writes SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache ● Insert into MemTable ● Dump to CommitLog ● No read ● Very Fast! ● Blocks on CPU before O/I! Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter
C* Data Model: Writes SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache ● Insert into MemTable ● Dump to CommitLog ● No read ● Very Fast! ● Blocks on CPU before O/I! Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter
SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
Cassandra Pain Points • Twitter Example • My tweets o SET tweets[JnBrymn][2013-07-19 T 09:20] = “Wonderful morning. This coffee is great.”! o SET tweets[JnBrymn][2013-07-19 T 09:21] = “Oops, smoke is coming out of the SQL server!”! o SET tweets[JnBrymn][2013-07-19 T 09:51] = “Now my coffee is cold :-(” • Get John’s tweets o GET tweets[JnBrymn] (output is as expected) OpenSource Connections
Cassandra Pain Points • Twitter Example • My tweets o SET tweets[JnBrymn][2013-07-19 T 09:20] = “Wonderful morning. This coffee is great.”! o SET tweets[JnBrymn][2013-07-19 T 09:21] = “Oops, smoke is coming out of the SQL server!”! o SET tweets[JnBrymn][2013-07-19 T 09:51] = “Now my coffee is cold :-(” • Get John’s tweets o GET tweets[JnBrymn] (output is as expected) • Pain-point: schema-less means that you have to read code to understand data model OpenSource Connections
Cassandra Pain Points • My timeline (other’s tweets) • More complicated – must store corresponding user names • Bad Option 1: keep multiple column families o SET timeline_from[JnBrymn][2013-07-19 T 09:20] = “softwaredoug”! o SET timeline_text[JnBrymn][2013-07-19 T 09:20] = “Hey John I posted on reddit, upvote me!” • Get John’s timeline o GET timeline_from[JnBrymn]! o GET timeline_text[JnBrymn]! OpenSource Connections
Cassandra Pain Points • My timeline (other’s tweets) • More complicated – must store corresponding user names • Bad Option 1: keep multiple column families o SET timeline_from[JnBrymn][2013-07-19 T 09:20] = “softwaredoug”! o SET timeline_text[JnBrymn][2013-07-19 T 09:20] = “Hey John I posted on reddit, upvote me!” • Get John’s timeline o GET timeline_from[JnBrymn]! o GET timeline_text[JnBrymn]! • Pain-point: Multiple queries required. OpenSource Connections
Cassandra Pain Points • My timeline • Bad Option 2: shove into single column value o SET timeline[JnBrymn][2013-07-19 T 09:20] = {from:”softwaredoug”, text: “Hey John I posted on reddit, upvote me!” • Get John’s timeline o GET timeline[JnBrymn] (…not too bad.)! OpenSource Connections
Cassandra Pain Points • My timeline • Bad Option 2: shove into single column value o SET timeline[JnBrymn][2013-07-19 T 09:20] = {from:”softwaredoug”, text: “Hey John I posted on reddit, upvote me!” • Get John’s timeline o GET timeline[JnBrymn] (…not too bad.)! • Pain-point: Updates require a read-then-modify OpenSource Connections
Cassandra Pain Points • My timeline • Best Option: composite column names o SET timeline[JnBrymn][2013-07-19 T 09:20|from] = ”softwaredoug”! o SET timeline[JnBrymn][2013-07-19 T 09:20|text] = “Hey John, I posted on reddit, upvote me!” • Get John’s timeline o GET timeline[JnBrymn] (extract from and text in client) • Resolves prior pain points! Scales well!! OpenSource Connections
Cassandra Pain Points • My timeline • Best Option: composite column names o SET timeline[JnBrymn][2013-07-19 T 09:20|from] = ”softwaredoug”! o SET timeline[JnBrymn][2013-07-19 T 09:20|text] = “Hey John, I posted on reddit, upvote me!” • Get John’s timeline o GET timeline[JnBrymn] (extract from and text in client) • Resolves prior pain points! Scales well!! • Pain-point: Even more code reading to understand data model! OpenSource Connections
Cassandra Pain Points • Justin Bieber’s timeline (e.g. many tweets) • Previous solution fails if number of columns > 2Billion • Best Option: composite row names o SET timeline[bieber|2013-07][19 T 09:20|from] = ”softwaredoug”! o SET timeline[bieber|2013-07][19 T 09:20|text] = “Justin Bieber, you complete me.” • Get Justin’s timeline o GET timeline[bieber|2013-07] (get other months too) OpenSource Connections
Cassandra Pain Points • Justin Bieber’s timeline (e.g. many tweets) • Previous solution fails if number of columns > 2Billion • Best Option: composite row names o SET timeline[bieber|2013-07][19 T 09:20|from] = ”softwaredoug”! o SET timeline[bieber|2013-07][19 T 09:20|text] = “Justin Bieber, you complete me.” • Get Justin’s timeline o GET timeline[bieber|2013-07] (get other months too) • Pain-point: Even more code reading to understand data model! OpenSource Connections
Introducing CQL • CQL is a reintroduction of schema so that you don’t have to read code to understand the data model. • CQL creates a common language so that details of the data model can be easily communicated. • CQL is a best-practices Cassandra interface and hides the messy details. OpenSource Connections
Introducing CQL • CQL is a reintroduction of schema so that you don’t have to read code to understand the data model. • CQL creates a common language so that details of the data model can be easily communicated. • CQL is a best-practices Cassandra interface and hides the messy details. OpenSource Connections Let’s see it!
Introducing CQL CREATE TABLE users ( id timeuuid PRIMARY KEY,! lastname varchar,! firstname varchar,! dateOfBirth timestamp );! ! ! ! OpenSource Connections
Introducing CQL CREATE TABLE users ( id timeuuid PRIMARY KEY,! lastname varchar,! firstname varchar,! dateOfBirth timestamp );! ! INSERT INTO users (id,lastname, firstname, dateofbirth) VALUES (now(),'Berryman',’John','1975-09-15');! !! OpenSource Connections
Introducing CQL CREATE TABLE users ( id timeuuid PRIMARY KEY,! lastname varchar,! firstname varchar,! dateOfBirth timestamp );! ! INSERT INTO users (id,lastname, firstname, dateofbirth) VALUES (now(),’Berryman’,’John’,’1975-09-15’);! ! UPDATE users SET firstname = ’John’ WHERE id = f74c0b20-0862-11e3-8cf6-b74c10b01fc6;! ! OpenSource Connections
Introducing CQL CREATE TABLE users ( id timeuuid PRIMARY KEY,! lastname varchar,! firstname varchar,! dateOfBirth timestamp );! ! INSERT INTO users (id,lastname, firstname, dateofbirth) VALUES (now(),'Berryman',’John','1975-09-15');! ! UPDATE users SET firstname = 'John’ WHERE id = f74c0b20-0862-11e3-8cf6-b74c10b01fc6;! ! SELECT dateofbirth,firstname,lastname FROM users ;! ! dateofbirth | firstname | lastname! --------------------------+-----------+----------! 1975-09-15 00:00:00-0400 | John | Berryman! OpenSource Connections
Introducing CQL “Hey sweet! It’s exactly the same as MySQL!” OpenSource Connections
Introducing CQL “Hey sweet! It’s exactly the same as MySQL!” Hold your horses. There are some important differences. OpenSource Connections
Introducing CQL “Hey sweet! It’s exactly the same as MySQL!” Hold your horses. There are some important differences. “Wait? What happened to the Cassandra’s wide rows?” OpenSource Connections
Introducing CQL “Hey sweet! It’s exactly the same as MySQL!” Hold your horses. There are some important differences. “Wait? What happened to the Cassandra’s wide rows?” There’s still there. Understanding the mapping is crucial! OpenSource Connections
Introducing CQL “Hey sweet! It’s exactly the same as MySQL!” Hold your horses. There are some important differences. “Wait? What happened to the Cassandra’s wide rows?” There’s still there. Understanding the mapping is crucial! OpenSource Connections Remember this: • Cassandra finds rows fast • Cassandra scans columns fast • Cassandra does not scan rows
The CQL/Cassandra Mapping CREATE TABLE employees (! name text PRIMARY KEY,! age int,! role text! );! ! OpenSource Connections
The CQL/Cassandra Mapping CREATE TABLE employees (! name text PRIMARY KEY,! age int,! role text! );! ! name | age | role! -----+-----+-----! john | 37 | dev! eric | 38 | ceo! ! OpenSource Connections
The CQL/Cassandra Mapping CREATE TABLE employees (! name text PRIMARY KEY,! age int,! role text! );! ! name | age | role! -----+-----+-----! john | 37 | dev! eric | 38 | ceo! ! OpenSource Connections age! role! john! 37! dev! age! role! eric! 38! ceo!
The CQL/Cassandra Mapping CREATE TABLE employees (! company text,! name text,! age int,! role text,! PRIMARY KEY (company,name)! );! ! OpenSource Connections
The CQL/Cassandra Mapping CREATE TABLE employees (! company text,! name text,! age int,! role text,! PRIMARY KEY (company,name)! );! ! company | name | age | role! --------+------+-----+-----! OSC | eric | 38 | ceo! OSC | john | 37 | dev! RKG | anya | 29 | lead! RKG | ben | 27 | dev! RKG | chad | 35 | ops! OpenSource Connections
The CQL/Cassandra Mapping CREATE TABLE employees (! company text,! name text,! age int,! role text,! PRIMARY KEY (company,name)! );! ! company | name | age | role! --------+------+-----+-----! OSC | eric | 38 | ceo! OSC | john | 37 | dev! RKG | anya | 29 | lead! RKG | ben | 27 | dev! RKG | chad | 35 | ops! ! eric:age! eric:role! john:age! john:role! OSC! 38! dev! 37! dev! anya:age! anya:role! ben:age! ben:role! chad:age! chad:role! RKG! 29! lead! 27! dev! 35! ops! OpenSource Connections
The CQL/Cassandra Mapping CREATE TABLE example (! A text,! B text,! C text,! D text,! E text,! F text,! PRIMARY KEY ((A,B),C,D)! );! OpenSource Connections
The CQL/Cassandra Mapping CREATE TABLE example (! A text,! B text,! C text,! D text,! E text,! F text,! PRIMARY KEY ((A,B),C,D)! );! A | B | C | D | E | F ! --+---+---+---+---+---! a | b | c | d | e | f! a | b | c | g | h | i! a | b | j | k | l | m! a | n | o | p | q | r! s | t | u | v | w | x! OpenSource Connections
The CQL/Cassandra Mapping CREATE TABLE example (! A text,! B text,! C text,! D text,! E text,! F text,! PRIMARY KEY ((A,B),C,D)! );! A | B | C | D | E | F ! --+---+---+---+---+---! a | b | c | d | e | f! a | b | c | g | h | i! a | b | j | k | l | m! a | n | o | p | q | r! s | t | u | v | w | x! ! c:d:E! c:d:F! c:g:E! c:g:F! j:k:E! j:k:F! a:b! e! f! h! i! l! m! OpenSource Connections ! o:p:E! o:p:F! a:n! q! r! ! u:v:E! u:v:F! s:t! w! x!
CQL for Sets, Lists, and Maps • Collection Semantics o Sets hold list of unique elements o Lists hold ordered, possibly repeating elements o Maps hold a list of key-value pairs • Uses same old Cassandra data structure OpenSource Connections
CQL for Sets, Lists, and Maps • Collection Semantics o Sets hold list of unique elements o Lists hold ordered, possibly repeating elements o Maps hold a list of key-value pairs • Uses same old Cassandra data structure • Declaring OpenSource Connections CREATE TABLE mytable(! X text,! Y text,! myset set<text>,! mylist list<int>,! mymap map<text, text>,! PRIMARY KEY (X,Y)! );!
CQL for Sets, Lists, and Maps • Collection Semantics o Sets hold list of unique elements o Lists hold ordered, possibly repeating elements o Maps hold a list of key-value pairs • Uses same old Cassandra data structure • Declaring OpenSource Connections CREATE TABLE mytable(! X text,! Y text,! myset set<text>,! mylist list<int>,! mymap map<text, text>,! PRIMARY KEY (X,Y)! );! Collection fields can not be used in primary keys
CQL for Sets, Lists, and Maps • Inserting ! INSERT INTO mytable (row, myset) VALUES (123, { ‘apple’, ‘banana’}); OpenSource Connections
CQL for Sets, Lists, and Maps • Inserting ! INSERT INTO mytable (row, myset) VALUES (123, { ‘apple’, ‘banana’}); INSERT INTO mytable (row, mylist) VALUES (123, [‘apple’,’banana’,’apple’]); OpenSource Connections
CQL for Sets, Lists, and Maps • Inserting ! INSERT INTO mytable (row, myset) VALUES (123, { ‘apple’, ‘banana’}); INSERT INTO mytable (row, mylist) VALUES (123, [‘apple’,’banana’,’apple’]); INSERT INTO mytable (row, mymap) VALUES (123, {1:’apple’,2:’banana’}) OpenSource Connections
CQL for Sets, Lists, and Maps • Updating UPDATE mytable SET myset = myset + {‘apple’,‘banana’} WHERE row = 123; UPDATE mytable SET myset = myset - { ‘apple’ } WHERE row = 123;! OpenSource Connections
CQL for Sets, Lists, and Maps • Updating UPDATE mytable SET myset = myset + {‘apple’,‘banana’} WHERE row = 123; UPDATE mytable SET myset = myset - { ‘apple’ } WHERE row = 123;! UPDATE mytable SET mylist = mylist + [‘apple’,‘banana’] WHERE row = 123; UPDATE mytable SET mylist = [‘banana’] + mylist WHERE row = 123;! OpenSource Connections
CQL for Sets, Lists, and Maps • Updating UPDATE mytable SET myset = myset + {‘apple’,‘banana’} WHERE row = 123; UPDATE mytable SET myset = myset - { ‘apple’ } WHERE row = 123;! UPDATE mytable SET mylist = mylist + [‘apple’,‘banana’] WHERE row = 123; UPDATE mytable SET mylist = [‘banana’] + mylist WHERE row = 123;! UPDATE mytable SET mymap[‘fruit’] = ‘apple’ WHERE row = 123 UPDATE mytable SET mymap = mymap + { ‘fruit’:‘apple’} WHERE row = 123! OpenSource Connections
CQL for Sets, Lists, and Maps SETS ! CREATE TABLE mytable(! X text,! Y text,! myset set<int>,! PRIMARY KEY (X,Y)! );! ! OpenSource Connections
CQL for Sets, Lists, and Maps SETS ! CREATE TABLE mytable(! X text,! Y text,! myset set<int>,! PRIMARY KEY (X,Y)! );! ! X | Y | myset ! ---+---+------------! a | b | {1,2}! a | c | {3,4,5}! OpenSource Connections
CQL for Sets, Lists, and Maps SETS ! CREATE TABLE mytable(! X text,! Y text,! myset set<int>,! PRIMARY KEY (X,Y)! );! ! X | Y | myset ! ---+---+------------! a | b | {1,2}! a | c | {3,4,5}! ! b:myset:1! b:myset:2! c:myset:3! c:myset:4! c:myset:5! a! OpenSource Connections
CQL for Sets, Lists, and Maps OpenSource Connections LISTS ! CREATE TABLE mytable(! X text,! Y text,! mylist list<int>,! PRIMARY KEY (X,Y)! );! !
CQL for Sets, Lists, and Maps X | Y | mylist ! ---+---+------------! a | b | [1,2]! OpenSource Connections LISTS ! CREATE TABLE mytable(! X text,! Y text,! mylist list<int>,! PRIMARY KEY (X,Y)! );! !
CQL for Sets, Lists, and Maps X | Y | mylist ! ---+---+------------! a | b | [1,2]! ! b:mylist:f7e5450039..8d! b:mylist:f7e5450139..8d! a! 1! 2! OpenSource Connections LISTS ! CREATE TABLE mytable(! X text,! Y text,! mylist list<int>,! PRIMARY KEY (X,Y)! );! !
CQL for Sets, Lists, and Maps X | Y | mylist ! ---+---+------------! a | b | [1,2]! ! b:mylist:f7e5450039..8d! b:mylist:f7e5450139..8d! a! 1! 2! OpenSource Connections LISTS ! CREATE TABLE mytable(! X text,! Y text,! mylist list<int>,! PRIMARY KEY (X,Y)! );! !
CQL for Sets, Lists, and Maps MAPS ! CREATE TABLE mytable(! X text,! Y text,! mymap map<text,int>,! PRIMARY KEY (X,Y)! );! ! OpenSource Connections
CQL for Sets, Lists, and Maps MAPS ! CREATE TABLE mytable(! X text,! Y text,! mymap map<text,int>,! PRIMARY KEY (X,Y)! );! ! X | Y | mymap ! ---+---+------------! a | b | {m:1,n:2}! a | c |{n:3,p:4,q:5}! OpenSource Connections
CQL for Sets, Lists, and Maps MAPS ! CREATE TABLE mytable(! X text,! Y text,! mymap map<text,int>,! PRIMARY KEY (X,Y)! );! ! X | Y | mymap ! ---+---+------------! a | b | {m:1,n:2}! a | c |{n:3,p:4,q:5}! ! b:mymap:m! b:mymap:n! c:mymap:n! c:mymap:p! c:mymap:q! a! 1! 2! 3! 4! 5! OpenSource Connections
Peek Behind the Scenes! Do it! (in cqlsh) CREATE KEYSPACE test WITH replication = ! {'class': 'SimpleStrategy', 'replication_factor': 1};! USE test;! CREATE TABLE stuff ( a int, b int, myset set<int>,! mylist list<int>, mymap map<int,int>, PRIMARY KEY (a,b));! UPDATE stuff SET myset = {1,2}, mylist = [3,4,5], mymap = {6:7,8:9} WHERE a = 0 AND b = 1;! SELECT * FROM stuff;! ! (in cassandra-cli) use test;! list stuff ;! ! (in cqlsh) SELECT key_aliases,column_aliases from system.schema_columnfamilies WHERE keyspace_name = 'test' AND columnfamily_name = 'stuff';! OpenSource Connections
Putting it All Together …you already know • CQL is a reintroduction of schema • CQL creates a common data modeling language • CQL is a best-practices Cassandra interface . OpenSource Connections
Putting it All Together …you already know • CQL is a reintroduction of schema • CQL creates a common data modeling language • CQL is a best-practices Cassandra interface …now you know • CQL let’s you take advantage of the C* Data structure . OpenSource Connections
Putting it All Together …you already know • CQL is a reintroduction of schema • CQL creates a common data modeling language • CQL is a best-practices Cassandra interface …now you know • CQL let’s you take advantage of the C* Data structure …but also • CQL protocol is binary and therefore interoperable with any language • CQL is asynchronous and fast (Thrift transport layer is synchronous) • CQL allows the possibility for prepared statements OpenSource Connections
Thanks! Follow me on Twitter @JnBrymn Check out the OpenSource Connection Blog http://www.opensourceconnections.com/blog/ OpenSource Connections

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Cassandra Summit 2014: Understanding CQL3 Inside and Out

  • 1.
    The CQL3/Cassandra Mapping John Berryman OpenSource Connections OpenSource Connections
  • 2.
    Outline • WhatProblem does CQL Solve? • The Cassandra Data Model • Pain Points of “Old” Cassandra • Introducing CQL • Understanding the CQL/Cassandra Mapping • CQL for Sets, Lists, and Maps • Putting it All Together OpenSource Connections
  • 3.
    What Problem doesCQL Solve? • The Awesomeness that is Cassandra: o Distributed columnar data store o No single point of failure o Optimized for availability (though “Tunably” consistent) o Optimized for writes o Easily maintainable o Almost infinitely scalable . OpenSource Connections
  • 4.
    What Problem doesCQL Solve? • The Awesomeness that is Cassandra: o Distributed columnar data store o No single point of failure o Optimized for availability (though “Tunably” consistent) o Optimized for writes o Easily maintainable o Almost infinitely scalable • Cassandra’s usability challenges o NoSQL – “Where are my JOINS? No Schema? De-normalize!?” o BigTable – “Tables with millions of columns!?” . OpenSource Connections
  • 5.
    What Problem doesCQL Solve? • The Awesomeness that is Cassandra: o Distributed columnar data store o No single point of failure o Optimized for availability (though “Tunably” consistent) o Optimized for writes o Easily maintainable o Almost infinitely scalable • Cassandra’s usability challenges o NoSQL – “Where are my JOINS? No Schema? De-normalize!?” o BigTable – “Tables with millions of columns!?” • CQL saves the day! o A best-practices interface to Cassandra o Uses familiar SQL-like language OpenSource Connections
  • 6.
    C* Data Model OpenSource Connections Keyspace
  • 7.
    C* Data Model Keyspace Column Family Column Family OpenSource Connections
  • 8.
    C* Data Model Keyspace Column Family Column Family OpenSource Connections
  • 9.
    C* Data Model Keyspace Column Family Column Family OpenSource Connections
  • 10.
    C* Data Model OpenSource Connections Row Key
  • 11.
    C* Data Model OpenSource Connections Row Key Column Column Name Column Value (or Tombstone) Timestamp Time-to-live
  • 12.
    C* Data Model ● Row Key, Column Name, Column OpenSource Connections Value have types ● Column Name has comparator ● RowKey has partitioner ● Rows can have any number of columns - even in same column family ● Rows can have many columns ● Column Values can be omitted ● Time-to-live is useful! ● Tombstones Row Key Column Column Name Column Value (or Tombstone) Timestamp Time-to-live
  • 13.
    C* Data Model:Writes SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache ● Insert into MemTable ● Dump to CommitLog ● No read ● Very Fast! ● Blocks on CPU before O/I! Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter
  • 14.
    C* Data Model:Writes SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache ● Insert into MemTable ● Dump to CommitLog ● No read ● Very Fast! ● Blocks on CPU before O/I! Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter
  • 15.
    C* Data Model:Writes SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache ● Insert into MemTable ● Dump to CommitLog ● No read ● Very Fast! ● Blocks on CPU before O/I! Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter
  • 16.
    SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
  • 17.
    SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
  • 18.
    SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
  • 19.
    SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
  • 20.
    SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
  • 21.
    SSTable SSTable SSTable OpenSource Connections Mem Table CommitLog Row Cache Key Cache SSTable Key Cache Key Cache Key Cache Bloom Filter ● Get values from Memtable ● Get values from row cache if present ● Otherwise check bloom filter to find appropriate SSTables ● Check Key Cache for fast SSTable Search ● Get values from SSTables ● Repopulate Row Cache ● Super Fast Col. retrieval ● Fast row slicing C* Data Model: Reads
  • 22.
    Cassandra Pain Points • Twitter Example • My tweets o SET tweets[JnBrymn][2013-07-19 T 09:20] = “Wonderful morning. This coffee is great.”! o SET tweets[JnBrymn][2013-07-19 T 09:21] = “Oops, smoke is coming out of the SQL server!”! o SET tweets[JnBrymn][2013-07-19 T 09:51] = “Now my coffee is cold :-(” • Get John’s tweets o GET tweets[JnBrymn] (output is as expected) OpenSource Connections
  • 23.
    Cassandra Pain Points • Twitter Example • My tweets o SET tweets[JnBrymn][2013-07-19 T 09:20] = “Wonderful morning. This coffee is great.”! o SET tweets[JnBrymn][2013-07-19 T 09:21] = “Oops, smoke is coming out of the SQL server!”! o SET tweets[JnBrymn][2013-07-19 T 09:51] = “Now my coffee is cold :-(” • Get John’s tweets o GET tweets[JnBrymn] (output is as expected) • Pain-point: schema-less means that you have to read code to understand data model OpenSource Connections
  • 24.
    Cassandra Pain Points • My timeline (other’s tweets) • More complicated – must store corresponding user names • Bad Option 1: keep multiple column families o SET timeline_from[JnBrymn][2013-07-19 T 09:20] = “softwaredoug”! o SET timeline_text[JnBrymn][2013-07-19 T 09:20] = “Hey John I posted on reddit, upvote me!” • Get John’s timeline o GET timeline_from[JnBrymn]! o GET timeline_text[JnBrymn]! OpenSource Connections
  • 25.
    Cassandra Pain Points • My timeline (other’s tweets) • More complicated – must store corresponding user names • Bad Option 1: keep multiple column families o SET timeline_from[JnBrymn][2013-07-19 T 09:20] = “softwaredoug”! o SET timeline_text[JnBrymn][2013-07-19 T 09:20] = “Hey John I posted on reddit, upvote me!” • Get John’s timeline o GET timeline_from[JnBrymn]! o GET timeline_text[JnBrymn]! • Pain-point: Multiple queries required. OpenSource Connections
  • 26.
    Cassandra Pain Points • My timeline • Bad Option 2: shove into single column value o SET timeline[JnBrymn][2013-07-19 T 09:20] = {from:”softwaredoug”, text: “Hey John I posted on reddit, upvote me!” • Get John’s timeline o GET timeline[JnBrymn] (…not too bad.)! OpenSource Connections
  • 27.
    Cassandra Pain Points • My timeline • Bad Option 2: shove into single column value o SET timeline[JnBrymn][2013-07-19 T 09:20] = {from:”softwaredoug”, text: “Hey John I posted on reddit, upvote me!” • Get John’s timeline o GET timeline[JnBrymn] (…not too bad.)! • Pain-point: Updates require a read-then-modify OpenSource Connections
  • 28.
    Cassandra Pain Points • My timeline • Best Option: composite column names o SET timeline[JnBrymn][2013-07-19 T 09:20|from] = ”softwaredoug”! o SET timeline[JnBrymn][2013-07-19 T 09:20|text] = “Hey John, I posted on reddit, upvote me!” • Get John’s timeline o GET timeline[JnBrymn] (extract from and text in client) • Resolves prior pain points! Scales well!! OpenSource Connections
  • 29.
    Cassandra Pain Points • My timeline • Best Option: composite column names o SET timeline[JnBrymn][2013-07-19 T 09:20|from] = ”softwaredoug”! o SET timeline[JnBrymn][2013-07-19 T 09:20|text] = “Hey John, I posted on reddit, upvote me!” • Get John’s timeline o GET timeline[JnBrymn] (extract from and text in client) • Resolves prior pain points! Scales well!! • Pain-point: Even more code reading to understand data model! OpenSource Connections
  • 30.
    Cassandra Pain Points • Justin Bieber’s timeline (e.g. many tweets) • Previous solution fails if number of columns > 2Billion • Best Option: composite row names o SET timeline[bieber|2013-07][19 T 09:20|from] = ”softwaredoug”! o SET timeline[bieber|2013-07][19 T 09:20|text] = “Justin Bieber, you complete me.” • Get Justin’s timeline o GET timeline[bieber|2013-07] (get other months too) OpenSource Connections
  • 31.
    Cassandra Pain Points • Justin Bieber’s timeline (e.g. many tweets) • Previous solution fails if number of columns > 2Billion • Best Option: composite row names o SET timeline[bieber|2013-07][19 T 09:20|from] = ”softwaredoug”! o SET timeline[bieber|2013-07][19 T 09:20|text] = “Justin Bieber, you complete me.” • Get Justin’s timeline o GET timeline[bieber|2013-07] (get other months too) • Pain-point: Even more code reading to understand data model! OpenSource Connections
  • 32.
    Introducing CQL •CQL is a reintroduction of schema so that you don’t have to read code to understand the data model. • CQL creates a common language so that details of the data model can be easily communicated. • CQL is a best-practices Cassandra interface and hides the messy details. OpenSource Connections
  • 33.
    Introducing CQL •CQL is a reintroduction of schema so that you don’t have to read code to understand the data model. • CQL creates a common language so that details of the data model can be easily communicated. • CQL is a best-practices Cassandra interface and hides the messy details. OpenSource Connections Let’s see it!
  • 34.
    Introducing CQL CREATETABLE users ( id timeuuid PRIMARY KEY,! lastname varchar,! firstname varchar,! dateOfBirth timestamp );! ! ! ! OpenSource Connections
  • 35.
    Introducing CQL CREATETABLE users ( id timeuuid PRIMARY KEY,! lastname varchar,! firstname varchar,! dateOfBirth timestamp );! ! INSERT INTO users (id,lastname, firstname, dateofbirth) VALUES (now(),'Berryman',’John','1975-09-15');! !! OpenSource Connections
  • 36.
    Introducing CQL CREATETABLE users ( id timeuuid PRIMARY KEY,! lastname varchar,! firstname varchar,! dateOfBirth timestamp );! ! INSERT INTO users (id,lastname, firstname, dateofbirth) VALUES (now(),’Berryman’,’John’,’1975-09-15’);! ! UPDATE users SET firstname = ’John’ WHERE id = f74c0b20-0862-11e3-8cf6-b74c10b01fc6;! ! OpenSource Connections
  • 37.
    Introducing CQL CREATETABLE users ( id timeuuid PRIMARY KEY,! lastname varchar,! firstname varchar,! dateOfBirth timestamp );! ! INSERT INTO users (id,lastname, firstname, dateofbirth) VALUES (now(),'Berryman',’John','1975-09-15');! ! UPDATE users SET firstname = 'John’ WHERE id = f74c0b20-0862-11e3-8cf6-b74c10b01fc6;! ! SELECT dateofbirth,firstname,lastname FROM users ;! ! dateofbirth | firstname | lastname! --------------------------+-----------+----------! 1975-09-15 00:00:00-0400 | John | Berryman! OpenSource Connections
  • 38.
    Introducing CQL “Heysweet! It’s exactly the same as MySQL!” OpenSource Connections
  • 39.
    Introducing CQL “Heysweet! It’s exactly the same as MySQL!” Hold your horses. There are some important differences. OpenSource Connections
  • 40.
    Introducing CQL “Heysweet! It’s exactly the same as MySQL!” Hold your horses. There are some important differences. “Wait? What happened to the Cassandra’s wide rows?” OpenSource Connections
  • 41.
    Introducing CQL “Heysweet! It’s exactly the same as MySQL!” Hold your horses. There are some important differences. “Wait? What happened to the Cassandra’s wide rows?” There’s still there. Understanding the mapping is crucial! OpenSource Connections
  • 42.
    Introducing CQL “Heysweet! It’s exactly the same as MySQL!” Hold your horses. There are some important differences. “Wait? What happened to the Cassandra’s wide rows?” There’s still there. Understanding the mapping is crucial! OpenSource Connections Remember this: • Cassandra finds rows fast • Cassandra scans columns fast • Cassandra does not scan rows
  • 43.
    The CQL/Cassandra Mapping CREATE TABLE employees (! name text PRIMARY KEY,! age int,! role text! );! ! OpenSource Connections
  • 44.
    The CQL/Cassandra Mapping CREATE TABLE employees (! name text PRIMARY KEY,! age int,! role text! );! ! name | age | role! -----+-----+-----! john | 37 | dev! eric | 38 | ceo! ! OpenSource Connections
  • 45.
    The CQL/Cassandra Mapping CREATE TABLE employees (! name text PRIMARY KEY,! age int,! role text! );! ! name | age | role! -----+-----+-----! john | 37 | dev! eric | 38 | ceo! ! OpenSource Connections age! role! john! 37! dev! age! role! eric! 38! ceo!
  • 46.
    The CQL/Cassandra Mapping CREATE TABLE employees (! company text,! name text,! age int,! role text,! PRIMARY KEY (company,name)! );! ! OpenSource Connections
  • 47.
    The CQL/Cassandra Mapping CREATE TABLE employees (! company text,! name text,! age int,! role text,! PRIMARY KEY (company,name)! );! ! company | name | age | role! --------+------+-----+-----! OSC | eric | 38 | ceo! OSC | john | 37 | dev! RKG | anya | 29 | lead! RKG | ben | 27 | dev! RKG | chad | 35 | ops! OpenSource Connections
  • 48.
    The CQL/Cassandra Mapping CREATE TABLE employees (! company text,! name text,! age int,! role text,! PRIMARY KEY (company,name)! );! ! company | name | age | role! --------+------+-----+-----! OSC | eric | 38 | ceo! OSC | john | 37 | dev! RKG | anya | 29 | lead! RKG | ben | 27 | dev! RKG | chad | 35 | ops! ! eric:age! eric:role! john:age! john:role! OSC! 38! dev! 37! dev! anya:age! anya:role! ben:age! ben:role! chad:age! chad:role! RKG! 29! lead! 27! dev! 35! ops! OpenSource Connections
  • 49.
    The CQL/Cassandra Mapping CREATE TABLE example (! A text,! B text,! C text,! D text,! E text,! F text,! PRIMARY KEY ((A,B),C,D)! );! OpenSource Connections
  • 50.
    The CQL/Cassandra Mapping CREATE TABLE example (! A text,! B text,! C text,! D text,! E text,! F text,! PRIMARY KEY ((A,B),C,D)! );! A | B | C | D | E | F ! --+---+---+---+---+---! a | b | c | d | e | f! a | b | c | g | h | i! a | b | j | k | l | m! a | n | o | p | q | r! s | t | u | v | w | x! OpenSource Connections
  • 51.
    The CQL/Cassandra Mapping CREATE TABLE example (! A text,! B text,! C text,! D text,! E text,! F text,! PRIMARY KEY ((A,B),C,D)! );! A | B | C | D | E | F ! --+---+---+---+---+---! a | b | c | d | e | f! a | b | c | g | h | i! a | b | j | k | l | m! a | n | o | p | q | r! s | t | u | v | w | x! ! c:d:E! c:d:F! c:g:E! c:g:F! j:k:E! j:k:F! a:b! e! f! h! i! l! m! OpenSource Connections ! o:p:E! o:p:F! a:n! q! r! ! u:v:E! u:v:F! s:t! w! x!
  • 52.
    CQL for Sets,Lists, and Maps • Collection Semantics o Sets hold list of unique elements o Lists hold ordered, possibly repeating elements o Maps hold a list of key-value pairs • Uses same old Cassandra data structure OpenSource Connections
  • 53.
    CQL for Sets,Lists, and Maps • Collection Semantics o Sets hold list of unique elements o Lists hold ordered, possibly repeating elements o Maps hold a list of key-value pairs • Uses same old Cassandra data structure • Declaring OpenSource Connections CREATE TABLE mytable(! X text,! Y text,! myset set<text>,! mylist list<int>,! mymap map<text, text>,! PRIMARY KEY (X,Y)! );!
  • 54.
    CQL for Sets,Lists, and Maps • Collection Semantics o Sets hold list of unique elements o Lists hold ordered, possibly repeating elements o Maps hold a list of key-value pairs • Uses same old Cassandra data structure • Declaring OpenSource Connections CREATE TABLE mytable(! X text,! Y text,! myset set<text>,! mylist list<int>,! mymap map<text, text>,! PRIMARY KEY (X,Y)! );! Collection fields can not be used in primary keys
  • 55.
    CQL for Sets,Lists, and Maps • Inserting ! INSERT INTO mytable (row, myset) VALUES (123, { ‘apple’, ‘banana’}); OpenSource Connections
  • 56.
    CQL for Sets,Lists, and Maps • Inserting ! INSERT INTO mytable (row, myset) VALUES (123, { ‘apple’, ‘banana’}); INSERT INTO mytable (row, mylist) VALUES (123, [‘apple’,’banana’,’apple’]); OpenSource Connections
  • 57.
    CQL for Sets,Lists, and Maps • Inserting ! INSERT INTO mytable (row, myset) VALUES (123, { ‘apple’, ‘banana’}); INSERT INTO mytable (row, mylist) VALUES (123, [‘apple’,’banana’,’apple’]); INSERT INTO mytable (row, mymap) VALUES (123, {1:’apple’,2:’banana’}) OpenSource Connections
  • 58.
    CQL for Sets,Lists, and Maps • Updating UPDATE mytable SET myset = myset + {‘apple’,‘banana’} WHERE row = 123; UPDATE mytable SET myset = myset - { ‘apple’ } WHERE row = 123;! OpenSource Connections
  • 59.
    CQL for Sets,Lists, and Maps • Updating UPDATE mytable SET myset = myset + {‘apple’,‘banana’} WHERE row = 123; UPDATE mytable SET myset = myset - { ‘apple’ } WHERE row = 123;! UPDATE mytable SET mylist = mylist + [‘apple’,‘banana’] WHERE row = 123; UPDATE mytable SET mylist = [‘banana’] + mylist WHERE row = 123;! OpenSource Connections
  • 60.
    CQL for Sets,Lists, and Maps • Updating UPDATE mytable SET myset = myset + {‘apple’,‘banana’} WHERE row = 123; UPDATE mytable SET myset = myset - { ‘apple’ } WHERE row = 123;! UPDATE mytable SET mylist = mylist + [‘apple’,‘banana’] WHERE row = 123; UPDATE mytable SET mylist = [‘banana’] + mylist WHERE row = 123;! UPDATE mytable SET mymap[‘fruit’] = ‘apple’ WHERE row = 123 UPDATE mytable SET mymap = mymap + { ‘fruit’:‘apple’} WHERE row = 123! OpenSource Connections
  • 61.
    CQL for Sets,Lists, and Maps SETS ! CREATE TABLE mytable(! X text,! Y text,! myset set<int>,! PRIMARY KEY (X,Y)! );! ! OpenSource Connections
  • 62.
    CQL for Sets,Lists, and Maps SETS ! CREATE TABLE mytable(! X text,! Y text,! myset set<int>,! PRIMARY KEY (X,Y)! );! ! X | Y | myset ! ---+---+------------! a | b | {1,2}! a | c | {3,4,5}! OpenSource Connections
  • 63.
    CQL for Sets,Lists, and Maps SETS ! CREATE TABLE mytable(! X text,! Y text,! myset set<int>,! PRIMARY KEY (X,Y)! );! ! X | Y | myset ! ---+---+------------! a | b | {1,2}! a | c | {3,4,5}! ! b:myset:1! b:myset:2! c:myset:3! c:myset:4! c:myset:5! a! OpenSource Connections
  • 64.
    CQL for Sets,Lists, and Maps OpenSource Connections LISTS ! CREATE TABLE mytable(! X text,! Y text,! mylist list<int>,! PRIMARY KEY (X,Y)! );! !
  • 65.
    CQL for Sets,Lists, and Maps X | Y | mylist ! ---+---+------------! a | b | [1,2]! OpenSource Connections LISTS ! CREATE TABLE mytable(! X text,! Y text,! mylist list<int>,! PRIMARY KEY (X,Y)! );! !
  • 66.
    CQL for Sets,Lists, and Maps X | Y | mylist ! ---+---+------------! a | b | [1,2]! ! b:mylist:f7e5450039..8d! b:mylist:f7e5450139..8d! a! 1! 2! OpenSource Connections LISTS ! CREATE TABLE mytable(! X text,! Y text,! mylist list<int>,! PRIMARY KEY (X,Y)! );! !
  • 67.
    CQL for Sets,Lists, and Maps X | Y | mylist ! ---+---+------------! a | b | [1,2]! ! b:mylist:f7e5450039..8d! b:mylist:f7e5450139..8d! a! 1! 2! OpenSource Connections LISTS ! CREATE TABLE mytable(! X text,! Y text,! mylist list<int>,! PRIMARY KEY (X,Y)! );! !
  • 68.
    CQL for Sets,Lists, and Maps MAPS ! CREATE TABLE mytable(! X text,! Y text,! mymap map<text,int>,! PRIMARY KEY (X,Y)! );! ! OpenSource Connections
  • 69.
    CQL for Sets,Lists, and Maps MAPS ! CREATE TABLE mytable(! X text,! Y text,! mymap map<text,int>,! PRIMARY KEY (X,Y)! );! ! X | Y | mymap ! ---+---+------------! a | b | {m:1,n:2}! a | c |{n:3,p:4,q:5}! OpenSource Connections
  • 70.
    CQL for Sets,Lists, and Maps MAPS ! CREATE TABLE mytable(! X text,! Y text,! mymap map<text,int>,! PRIMARY KEY (X,Y)! );! ! X | Y | mymap ! ---+---+------------! a | b | {m:1,n:2}! a | c |{n:3,p:4,q:5}! ! b:mymap:m! b:mymap:n! c:mymap:n! c:mymap:p! c:mymap:q! a! 1! 2! 3! 4! 5! OpenSource Connections
  • 71.
    Peek Behind theScenes! Do it! (in cqlsh) CREATE KEYSPACE test WITH replication = ! {'class': 'SimpleStrategy', 'replication_factor': 1};! USE test;! CREATE TABLE stuff ( a int, b int, myset set<int>,! mylist list<int>, mymap map<int,int>, PRIMARY KEY (a,b));! UPDATE stuff SET myset = {1,2}, mylist = [3,4,5], mymap = {6:7,8:9} WHERE a = 0 AND b = 1;! SELECT * FROM stuff;! ! (in cassandra-cli) use test;! list stuff ;! ! (in cqlsh) SELECT key_aliases,column_aliases from system.schema_columnfamilies WHERE keyspace_name = 'test' AND columnfamily_name = 'stuff';! OpenSource Connections
  • 72.
    Putting it AllTogether …you already know • CQL is a reintroduction of schema • CQL creates a common data modeling language • CQL is a best-practices Cassandra interface . OpenSource Connections
  • 73.
    Putting it AllTogether …you already know • CQL is a reintroduction of schema • CQL creates a common data modeling language • CQL is a best-practices Cassandra interface …now you know • CQL let’s you take advantage of the C* Data structure . OpenSource Connections
  • 74.
    Putting it AllTogether …you already know • CQL is a reintroduction of schema • CQL creates a common data modeling language • CQL is a best-practices Cassandra interface …now you know • CQL let’s you take advantage of the C* Data structure …but also • CQL protocol is binary and therefore interoperable with any language • CQL is asynchronous and fast (Thrift transport layer is synchronous) • CQL allows the possibility for prepared statements OpenSource Connections
  • 75.
    Thanks! Follow meon Twitter @JnBrymn Check out the OpenSource Connection Blog http://www.opensourceconnections.com/blog/ OpenSource Connections