Patrick McFadin's presentation discusses the functionalities and use cases of Apache Cassandra and its integration with Apache Spark. He highlights Cassandra's features including reliability, performance, and scalability for handling large datasets, particularly through examples like a weather station data model. The presentation also outlines how to utilize the Spark-Cassandra connector for advanced data processing and analytics.

1. @PatrickMcFadin
Patrick McFadin
Chief Evangelist for Apache Spark at DataStax
Double Down with Apache Cassandra
and Spark
1

2. My Background
…ran into this problem

3. Gave it my best shot
shard 1 shard 2 shard 3 shard 4
router
client
Patrick,
All your wildest
dreams will come
true.

4. Just add complexity!

5. A new plan

6. Dynamo Paper(2007)
• How do we build a data store that is:
• Reliable
• Performant
• “Always On”
• Nothing new and shiny
• 24 papers cited
Evolutionary. Real. Computer Science
Also the basis for Riak and Voldemort

7. BigTable(2006)
• Richer data model
• 1 key. Lots of values
• Fast sequential access
• 38 Papers cited

8. Cassandra(2008)
• Distributed features of Dynamo
• Data Model and storage from
BigTable
• February 17, 2010 it graduated to
a top-level Apache project

9. 6 years. How’s it going?

10. Before you get too excited
Cassandra is not…

11. A Data Ocean or Pond., Lake
An In-Memory Database
A Key-Value Store
A magical database unicorn that farts rainbows

12. When to use…
Loose data model (joins, sub-selects)
Absolute consistency (aka gotta have ACID)
No need to use anything else
You’ll miss the long, candle lit dinners with your
Oracle rep that always end with “what’s your
budget look like this year?”
Oracle, MySQL, Postgres or <RDBMS>

13. When to use…
Uptime is a top priority
Unpredictable or high scaling requirements
Workload is transactional
Willing to put the time or effort into understanding
how Cassandra works and ow to use it.
Use Oracle when you want to count your money.
Use Cassandra when you want to make money.

14. Learn to use it right

15. Cassandra is…
• Shared nothing
• Masterless peer-to-peer
• Based on Dynamo

16. Use Case Example

17. Example 1: Weather Station
• Weather station collects data
• Cassandra stores in sequence
• Application reads in sequence

18. Use case
• Store data per weather station
• Store time series in order: first to last
• Get all data for one weather station
• Get data for a single date and time
• Get data for a range of dates and times
Needed Queries
Data Model to support queries

19. Data Model
• Weather Station Id and Time
are unique
• Store as many as needed
CREATE TABLE temperature (
weather_station text,
year int,
month int,
day int,
hour int,
temperature double,
PRIMARY KEY (weather_station,year,month,day,hour)
);
INSERT INTO temperature(weather_station,year,month,day,hour,temperature)
VALUES (‘10010:99999’,2005,12,1,7,-5.6);
INSERT INTO temperature(weather_station,year,month,day,hour,temperature)
VALUES (‘10010:99999’,2005,12,1,8,-5.1);
INSERT INTO temperature(weather_station,year,month,day,hour,temperature)
VALUES (‘10010:99999’,2005,12,1,9,-4.9);
INSERT INTO temperature(weather_station,year,month,day,hour,temperature)
VALUES (‘10010:99999’,2005,12,1,10,-5.3);

20. Storage Model - Logical View
2005:12:1:7
-5.6
2005:12:1:8
-5.1
2005:12:1:9
-4.9
SELECT weather_station,hour,temperature
FROM temperature
WHERE weatherstation_id='10010:99999';
10010:99999
10010:99999
10010:99999
weather_station hour temperature
2005:12:1:10
-5.3
10010:99999

21. 2005:12:1:12
-5.4
2005:12:1:11
-4.9-5.3-4.9-5.1
2005:12:1:7
-5.6
Storage Model - Disk Layout
2005:12:1:8 2005:12:1:9
10010:99999
2005:12:1:10
Merged, Sorted and Stored Sequentially
SELECT weather_station,hour,temperature
FROM temperature
WHERE weatherstation_id='10010:99999';

22. Primary key relationship
PRIMARY KEY (weather_station,year,month,day,hour)

23. Primary key relationship
PRIMARY KEY (weather_station,year,month,day,hour)
Partition Key

24. Primary key relationship
PRIMARY KEY (weather_station,year,month,day,hour)
Partition Key Clustering Columns

25. Primary key relationship
PRIMARY KEY (weather_station,year,month,day,hour)
Partition Key Clustering Columns
10010:99999

26. 2005:12:1:7
-5.6
Primary key relationship
PRIMARY KEY (weather_station,year,month,day,hour)
Partition Key Clustering Columns
10010:99999
-5.3-4.9-5.1
2005:12:1:8 2005:12:1:9 2005:12:1:10

27. Partition keys
10010:99999 Murmur3 Hash Token = 7224631062609997448
722266:13850 Murmur3 Hash Token = -6804302034103043898
INSERT INTO temperature(weather_station,year,month,day,hour,temperature)
VALUES (‘10010:99999’,2005,12,1,7,-5.6);
INSERT INTO temperature(weather_station,year,month,day,hour,temperature)
VALUES (‘722266:13850’,2005,12,1,7,-5.6);
Consistent hash. 128 bit number
between 2-63 and 264

28. Partition keys
10010:99999 Murmur3 Hash Token = 15
722266:13850 Murmur3 Hash Token = 77
INSERT INTO temperature(weather_station,year,month,day,hour,temperature)
VALUES (‘10010:99999’,2005,12,1,7,-5.6);
INSERT INTO temperature(weather_station,year,month,day,hour,temperature)
VALUES (‘722266:13850’,2005,12,1,7,-5.6);
For this example, let’s make it a
reasonable number

29. Writes & WAN replication
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75
DC1
DC1: RF=3
Node Primary Replica Replica
10.0.0.1 00-25 76-100 51-75
10.0.0.2 26-50 00-25 76-100
10.0.0.3 51-75 26-50 00-25
10.0.0.4 76-100 51-75 26-50
10.10.0.1
00-25
10.10.0.4
76-100
10.10.0.2
26-50
10.10.0.3
51-75
DC2
Node Primary Replica Replica
10.0.0.1 00-25 76-100 51-75
10.0.0.2 26-50 00-25 76-100
10.0.0.3 51-75 26-50 00-25
10.0.0.4 76-100 51-75 26-50
DC2: RF=3
Client
Insert Data
Partition Key = 15
Asynchronous Local Replication
Asynchronous WAN Replication

30. Locality
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75
DC1
DC1: RF=3
Node Primary Replica Replica
10.0.0.1 00-25 76-100 51-75
10.0.0.2 26-50 00-25 76-100
10.0.0.3 51-75 26-50 00-25
10.0.0.4 76-100 51-75 26-50
10.10.0.1
00-25
10.10.0.4
76-100
10.10.0.2
26-50
10.10.0.3
51-75
DC2
Node Primary Replica Replica
10.0.0.1 00-25 76-100 51-75
10.0.0.2 26-50 00-25 76-100
10.0.0.3 51-75 26-50 00-25
10.0.0.4 76-100 51-75 26-50
DC2: RF=3
Client
Get Data
Partition Key = 15
Client
Get Data
Partition Key = 15

31. Query patterns
• Range queries
• “Slice” operation on disk
SELECT weatherstation,hour,temperature
FROM temperature
WHERE weatherstation=‘10010:99999'
AND year = 2005 AND month = 12 AND day = 1
AND hour >= 7 AND hour <= 10;
Single seek on disk
2005:12:1:12
-5.4
2005:12:1:11
-4.9-5.3-4.9-5.1
2005:12:1:7
-5.6
2005:12:1:8 2005:12:1:9
10010:99999
2005:12:1:10
Partition key for locality

32. Query patterns
• Range queries
• “Slice” operation on disk
Programmers like this
Sorted by event_time
2005:12:1:7
-5.6
2005:12:1:8
-5.1
2005:12:1:9
-4.9
10010:99999
10010:99999
10010:99999
weather_station hour temperature
2005:12:1:10
-5.3
10010:99999
SELECT weatherstation,hour,temperature
FROM temperature
WHERE weatherstation=‘10010:99999'
AND year = 2005 AND month = 12 AND day = 1
AND hour >= 7 AND hour <= 10;

33. Cassandra - Reads

34. Coordinated reads

35. Consistency Level
• Set with every read and write
• ONE
• QUORUM - >51% replicas ack
• LOCAL_QUORUM - >51% replicas ack in local DC
• LOCAL_ONE - Read repair only in local DC
• TWO
• ALL - All replicas ack. Full consistency

36. QUORUM and availability

37. Rapid Read Protection
NONE

38. Cassandra and Spark

39. Cassandra & Spark: A Great Combo
Datastax: spark-cassandra-connector:
https://github.com/datastax/spark-cassandra-connector
•Both are Easy to Use
•Spark Can Help You Bridge Your Hadoop
and Cassandra Systems
•Use Spark Libraries, Caching on-top of
Cassandra-stored Data
•Combine Spark Streaming with Cassandra
Storage

40. Spark On Cassandra
•Server-Side filters (where clauses)
•Cross-table operations (JOIN, UNION, etc.)
•Data locality-aware (speed)
•Data transformation, aggregation, etc.
•Natural Time Series Integration

41. Apache Spark and Cassandra Open Source Stack
Cassandra

42. Spark Cassandra Connector
42

43. Spark Cassandra Connector
*Cassandra tables exposed as Spark RDDs
*Read from and write to Cassandra
*Mapping of C* tables and rows to Scala objects
*All Cassandra types supported and converted to Scala types
*Server side data selection
*Virtual Nodes support
*Use with Scala or Java
*Compatible with, Spark 1.1.0, Cassandra 2.1 & 2.0

44. Type Mapping
CQL Type Scala Type
ascii String
bigint Long
boolean Boolean
counter Long
decimal BigDecimal, java.math.BigDecimal
double Double
float Float
inet java.net.InetAddress
int Int
list Vector, List, Iterable, Seq, IndexedSeq, java.util.List
map Map, TreeMap, java.util.HashMap
set Set, TreeSet, java.util.HashSet
text, varchar String
timestamp Long, java.util.Date, java.sql.Date, org.joda.time.DateTime
timeuuid java.util.UUID
uuid java.util.UUID
varint BigInt, java.math.BigInteger
*nullable values Option

45. Spark Cassandra Connector
https://github.com/datastax/spark-­‐cassandra-­‐connector
Keyspace Table
Cassandra Spark
RDD[CassandraRow]
RDD[Tuples]
Bundled
and
Supported
with
DSE
4.5!

46. Spark Cassandra Connector uses the DataStax Java Driver to
Read from and Write to C*
Spark C*
Full Token
Range
Each Executor Maintains
a connection to the C*
Cluster
Spark
Executor
DataStax
Java Driver
Tokens 1-1000
Tokens 1001 -2000
Tokens …
RDD’s read into different
splits based on sets of tokens
Spark Cassandra Connector

47. Co-locate Spark and C* for
Best Performance
C*
C*C*
C*
Spark
Worker
Spark
Worker
Spark
Master
Spark
Worker
Running Spark Workers
on
the same nodes as your
C* Cluster will save
network hops when
reading and writing

48. Analytics Workload Isolation
Cassandra
+ Spark DC
Cassandra
Only DC
Online
App
Analytical
App
Mixed Load Cassandra Cluster

49. Connecting to Cassandra
// Import Cassandra-specific functions on SparkContext and RDD objects
import com.datastax.driver.spark._
// Spark connection options
val conf = new SparkConf(true)
.setMaster("spark://192.168.123.10:7077")
.setAppName("cassandra-demo")
.set("cassandra.connection.host", "192.168.123.10") // initial
contact
.set("cassandra.username", "cassandra")
.set("cassandra.password", "cassandra")
val sc = new SparkContext(conf)

50. Accessing Data
CREATE TABLE test.words (word text PRIMARY KEY, count int);
INSERT INTO test.words (word, count) VALUES ('bar', 30);
INSERT INTO test.words (word, count) VALUES ('foo', 20);
// Use table as RDD
val rdd = sc.cassandraTable("test", "words")
// rdd: CassandraRDD[CassandraRow] = CassandraRDD[0]
rdd.toArray.foreach(println)
// CassandraRow[word: bar, count: 30]
// CassandraRow[word: foo, count: 20]
rdd.columnNames // Stream(word, count)
rdd.size // 2
val firstRow = rdd.first // firstRow: CassandraRow = CassandraRow[word: bar,
count: 30]
firstRow.getInt("count") // Int = 30
*Accessing table above as RDD:

51. Saving Data
val newRdd = sc.parallelize(Seq(("cat", 40), ("fox", 50)))
// newRdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[2]
newRdd.saveToCassandra("test", "words", Seq("word", "count"))
SELECT * FROM test.words;
word | count
------+-------
bar | 30
foo | 20
cat | 40
fox | 50
(4 rows)
*RDD above saved to Cassandra:

52. Weather Station Analysis
• Weather station collects data
• Cassandra stores in sequence
• Spark rolls up data into new
tables
Windsor California
July 1, 2014
High: 73.4
Low : 51.4

53. Roll-up table(SparkSQL example)
CREATE TABLE daily_high_low (
weatherstation text,
date text,
high_temp double,
low_temp double,
PRIMARY KEY ((weatherstation,date))
);
• Weather Station Id and Date are
unique
• High and low temp for each day
SparkSQL> INSERT INTO TABLE
> daily_high_low
> SELECT
> weatherstation, to_date(year, day, hour) date, max(temperature) high_temp, min(temperature) low_temp
> FROM temperature
> GROUP BY weatherstation_id, year, month, day;
OK
Time taken: 2.345 seconds
functions aggregations

54. What just happened
• Data is read from temperature table
• Transformed
• Inserted into the daily_high_low table
Table:
temperature
Table:
daily_high_low
Read data
from table
Transform
Insert data
into table

55. Thank you!
Bring the questions
Follow me on twitter
@PatrickMcFadin