Manchester Hadoop Meetup: Cassandra Spark internals

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@chbatey
Christopher Batey 
Manchester Hadoop and Big Data Meetup

@chbatey
Who am I?
• Maintainer of Stubbed Cassandra
• Other OS projects: akka-persistence,
wiremock
• Advocate for Apache Cassandra
• Part time consultant

@chbatey
Agenda
• Why - running Spark + C*
• How - Spark partitions are built up
• Example - KillrWeather

Weather data streaming
Incoming
weather
events
Apache Kafka
Producer
Consumer
NodeGuardian
Dashboard

@chbatey
Run this your self
• https://github.com/killrweather/killrweather

@chbatey
Pop quiz!
• Spark RDD
• Spark partition
• Spark worker
• Spark task
• Cassandra row
• Cassandra partition
• Cassandra token range

@chbatey
org.apache.spark.rdd.RDD
• Resilient Distributed Dataset (RDD)
• Created through transformations on data (map,filter..) or other RDDs
• Immutable
• Partitioned
• Reusable

@chbatey
RDD Operations
• Transformations - Similar to Scala collections API
• Produce new RDDs
• filter, flatmap, map, distinct, groupBy, union, zip, reduceByKey, subtract
• Actions
• Require materialization of the records to generate a value
• collect: Array[T], count, fold, reduce..

Spark RDDs
Represent a Large
Amount of Data
Partitioned into Chunks
RDD
1 2 3
4 5 6
7 8 9Worker 2
Worker 1 Worker 3
Worker 4

Worker 2
Worker 1
Spark RDDs
Represent a Large
Amount of Data
Partitioned into Chunks
RDD
2
346
7 8 9
Worker 3
Worker 4
1 5

Cassandra table
CREATE TABLE daily_aggregate_precip (
weather_station text,
year int,
month int,
day int,
precipitation counter,
PRIMARY KEY ((weather_station), year, month, day)
)
PRIMARY KEY ((weatherstation_id),year,month,day)
Partition Key Clustering Columns

Cassandra Data is Distributed By Token Range

0
500
999

0
500
Node 1
Node 2
Node 3
Node 4

0
500
Node 1
Node 2
Node 3
Node 4
Without vnodes

0
500
Node 1
Node 2
Node 3
Node 4
With vnodes

@chbatey
Replication strategy
• 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
Goals
• Spark partitions made up of token ranges on the same
node
• Tasks to be executed on workers co-located with that
node
• Same(ish) amount of data in each Spark partition

Node 1
120-220
300-500
780-830
0-50
•spark.cassandra.input.split.size_in_mb 64
•system.size_estimates (# partitions & mean size)
•tokens per spark partition
The Connector Uses Information on the Node to Make  
Spark Partitions

Node 1
120-220
300-500
0-50
Spark Partitions
1
780-830

1
Node 1
120-220
300-500
0-50
Spark Partitions
780-830

2
1
Node 1 300-500
0-50
Spark Partitions
780-830

2
1
Node 1
300-400
0-50
Spark Partitions
780-830
400-500

21
Node 1
0-50
Spark Partitions
780-830
400-500

21
Node 1
0-50
Spark Partitions
780-830
400-500
3

21
Node 1
0-50
Spark Partitions
780-830
3
400-500

21
Node 1
0-50
Spark Partitions
780-830
3

4
21
Node 1
0-50
Spark Partitions
780-830
3

421
Node 1
Spark Partitions
3

@chbatey
Key classes
• CassandraTableScanRDD, CassandraRDD
- getPreferredLocations
• CassandraTableRowReaderProvider
- DataSizeEstimates - goes to C*
• CassandraPartitioner
- Gets ring information from the driver
• CassandraPartition
- endpoints
- tokenRanges

4
spark.cassandra.input.fetch.size_in_rows 50
Data is Retrieved Using the DataStax Java Driver
0-50780-830
Node 1

4
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 780 and token(pk) <= 830

4
0-50
780-830
Node 1
50 CQL Rows

4
0-50
780-830
Node 1
50 CQL Rows
50 CQL Rows

4
0-50
780-830
Node 1
50 CQL Rows50 CQL Rows

4
0-50
780-830
Node 1
50 CQL Rows

4
0-50
780-830
Node 1
50 CQL Rows
50 CQL Rows

4
spark.cassandra.input.page.row.size 50
0-50
780-830
Node 1
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows

4
spark.cassandra.input.page.row.size 50
0-50
780-830
Node 1
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows

@chbatey
Other bits and bobs
• LocalNodeFirstLoadBalancingPolicy

@chbatey
Then we’re into Spark land
• Spark partitions are made up of C* partitions that exist
on the same node
• C* connector tells Spark which workers to use via
information from the C* driver

Node 2
Node 1
RDD
2
346
7 8 9
Node 3
Node 4
1 5
The Spark Cassandra
Connector saveToCassandra
method can be called on
almost all RDDs
rdd.saveToCassandra("Keyspace","Table")

Node 11
Java
Driver
1,1,1
1,2,1
2,1,1
3,8,1
3,2,1
3,4,1
3,5,1
3,1,1
1,4,1
5,4,1
2,4,1
8,4,1
9,4,1
3,9,1

Node 11
Java
Driver
1,1,1
1,2,1
2,1,1
3,8,1
3,2,1
3,4,1
3,5,1
3,1,1
1,4,1
5,4,1
2,4,1
8,4,1
9,4,1
11,4, spark.cassandra.output.batch.grouping.key partition 
spark.cassandra.output.batch.size.rows 4
spark.cassandra.output.batch.grouping.buffer.size 3
spark.cassandra.output.concurrent.writes 2 
3,9,1

Node 11
Java
Driver
1,1,1
1,2,1
2,1,1
3,8,1
3,2,1
3,4,1
3,5,1
3,1,1
1,4,1
5,4,1
2,4,1
8,4,1
9,4,1
3,9,1
PK=1

Node 11
Java
Driver
1,1,1 1,2,1
2,1,1
3,8,1
3,2,1
3,4,1
3,5,1
3,1,1
1,4,1
5,4,1
2,4,1
8,4,1
9,4,1
3,9,1
PK=1

Node 11
Java
Driver
1,1,1 1,2,1
2,1,1
3,8,1
3,2,1
3,4,1
3,5,1
3,1,1
1,4,1
5,4,1
2,4,1
8,4,1
9,4,1
3,9,1
PK=1
PK=2

Node 11
Java
Driver
1,1,1 1,2,1
2,1,1
3,8,13,2,1 3,4,1 3,5,1
3,1,1
1,4,1
5,4,1
2,4,1
8,4,1
9,4,1
3,9,1
PK=1
PK=2
PK=3

Node 11
Java
Driver
1,1,1 1,2,1
2,1,1
1,4,1
5,4,1
2,4,1
8,4,1
9,4,1
11,4,3,9,1
3,1,1
spark.cassandra.output.batch.grouping.key partition 
PK=1
PK=2

Node 11
Java
Driver
1,1,1 1,2,1
2,1,1
3,1,1
1,4,1
5,4,1
2,4,1
8,4,1
9,4,1
11,4,3,9,1 spark.cassandra.output.batch.grouping.key partition 
PK=1
PK=2
PK=3

Node 11
Java
Driver
1,1,1 1,2,1
2,1,1
3,1,1
1,4,1
5,4,1
2,4,1
8,4,1
9,4,1
3,9,1
PK=1
PK=2
PK=3

Node 11
Java
Driver
2,1,1
3,1,1
5,4,1
2,4,1
8,4,1
9,4,1
3,9,1
PK=2
PK=3

Node 11
Java
Driver
2,1,1
3,1,1
5,4,1
2,4,1
8,4,1
9,4,1
3,9,1
PK=2
PK=3
PK=5

Node 11
Java
Driver
2,1,1
3,1,1
5,4,1
2,4,18,4,1
9,4,1
3,9,13,9,1
PK=2
PK=3
PK=5

Node 11
Java
Driver
2,1,1
3,1,1
5,4,1
2,4,18,4,1
9,4,1
3,9,13,9,1
Write Acknowledged
PK=2
PK=3
PK=5

Node 11
Java
Driver
2,1,1
3,1,1
5,4,1
2,4,1
9,4,1
8,4,1
3,9,1
PK=2
PK=3
PK=5

Node 11
Java
Driver
3,1,1
5,4,1
9,4,1
8,4,1
3,9,1
PK=3
PK=5

Node 11
Java
Driver
3,1,1
5,4,1
9,4,1
8,4,1
3,9,1
PK=8
PK=3
PK=5

Node 11
Java
Driver
9,4,1
3,1,1
5,4,1
8,4,1
3,9,1
PK=8
PK=3
PK=5

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

raw_weather_data
CREATE TABLE raw_weather_data (
weather_station text, // Composite of Air Force Datsav3 station number and NCDC WBAN numbe
year int, // Year collected
month int, // Month collected
day int, // Day collected
hour int, // Hour collected
temperature double, // Air temperature (degrees Celsius)
dewpoint double, // Dew point temperature (degrees Celsius)
pressure double, // Sea level pressure (hectopascals)
wind_direction int, // Wind direction in degrees. 0-359
wind_speed double, // Wind speed (meters per second)
sky_condition int, // Total cloud cover (coded, see format documentation)
sky_condition_text text, // Non-coded sky conditions
one_hour_precip double, // One-hour accumulated liquid precipitation (millimeters)
six_hour_precip double, // Six-hour accumulated liquid precipitation (millimeters)
PRIMARY KEY ((weather_station), year, month, day, hour)
) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC);
Reverses data in the storage engine.

Primary key relationship
PRIMARY KEY ((weatherstation_id),year,month,day,hour)

Partition Key

WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC);

10010:99999

2005:12:1:7:temp
-5.6
10010:99999
-5.1
2005:12:1:8:temp
-4.9
2005:12:1:9:temp
-5.3
2005:12:1:10:temp

Data Locality
weatherstation_id=‘10010:99999’ ?
1000 Node Cluster
You are here!

Query patterns
• Range queries
• “Slice” operation on disk
SELECT weatherstation,hour,temperature
FROM raw_weather_data
WHERE weatherstation_id=‘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
2005:12:1:7
-5.6-5.1
2005:12:1:8
-4.9
2005:12:1:9
10010:99999
-5.3
2005:12:1:10
Partition key for locality

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 raw_weather_data
WHERE weatherstation_id=‘10010:99999'
AND year = 2005 AND month = 12 AND day = 1
AND hour >= 7 AND hour <= 10;

weather_station
CREATE TABLE weather_station (
id text PRIMARY KEY, // Composite of Air Force Datsav3 station number and NCDC WBAN number
name text, // Name of reporting station
country_code text, // 2 letter ISO Country ID
state_code text, // 2 letter state code for US stations
call_sign text, // International station call sign
lat double, // Latitude in decimal degrees
long double, // Longitude in decimal degrees
elevation double // Elevation in meters
);
Lookup table

daily_aggregate_temperature
CREATE TABLE daily_aggregate_temperature (
year int,
month int,
day int,
high double,
low double,
mean double,
variance double,
stdev double,
) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC);
SELECT high, low FROM daily_aggregate_temperature
WHERE weather_station='010010:99999'
AND year=2005 AND month=12 AND day=3;
high | low
------+------
1.8 | -1.5

daily_aggregate_precip
year int,
month int,
day int,
SELECT precipitation FROM daily_aggregate_precip
WHERE weather_station='010010:99999'
AND year=2005 AND month=12 AND day>=1 AND day <= 7;
0
10
20
30
40
1 2 3 4 5 6 7
17
26
2
0
33
12
0

Weather Station Stream Analysis
• Weather station collects data
• Data processed in stream
• Data stored in Cassandra
Windsor California
Today
Rainfall total: 1.2cm
High: 73.4F
Low : 51.4F

Incoming data from Kafka
725030:14732,2008,01,01,00,5.0,-3.9,1020.4,270,4.6,2,0.0,0.0

@chbatey
Building an aggregate
year int,
month int,
day int,
CQL Counter

@chbatey
Batch job on the fly?
(count: 24, mean: 14.428150, stdev: 7.092196, max: 28.034969, min: 0.675863)
(count: 11242, mean: 8.921956, stdev: 7.428311, max: 29.997986, min: -2.200000)

Weather data streaming
Load
Generator or
Data import
Apache Kafka
Producer
Consumer
NodeGuardian
Dashboard

@chbatey
Summary
• Cassandra
- always-on operational database
• Spark
- Batch analytics
- Stream processing and saving back to Cassandra

@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/

Manchester Hadoop Meetup: Cassandra Spark internals

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Manchester Hadoop Meetup: Cassandra Spark internals