Kite SDK introduction for Portland Big Data

Kite SDK: It’s for developers
Ryan Blue, Software Engineer

Resources
©2014 Cloudera, Inc. All rights reserved.
• Kite guide
• http://tiny.cloudera.com/KiteGuide
• Dataset overview and intro
• http://tiny.cloudera.com/Datasets
• Command-line tutorial
• http://tiny.cloudera.com/KiteCLI
• Kite repository and examples
• https://github.com/kite-sdk/kite
• https://github.com/kite-sdk/kite-examples

Agenda
• Kite background
• Kite data

What problem does Kite solve?
• Accessibility for getting started
• Easy to get started, without being an expert
• Use before understanding
• Save time for experienced developers
• Off-the-shelf tools for common tasks
• Quickly iterate and test configurations

Kite Datasets: Motivation
• Focus on using data, not managing files
• Developers shouldn’t have to maintain data files
• Use through configuration, not code
• Need consistency across the platform

Application
Database
Data files
User code
Provided
Maintained by the database

Application Application
Database
Data files
Data files HBase
User code

Application ApplicationApplication
Database
Data files
Data files
Kite Data
HBase
Data files HBase
Maintained by the Kite

Kite Datasets: Goals
• Think in terms of data: datasets, views, records
• Describe data, layout and Kite does the right thing
• Should work consistently across the platform
• Reliable

Kite Datasets: Compatibility
Project HDFS (avro) HDFS (parquet) HBase
Kite 1.0 1.0 1.0
Flume Sink 1.0 1.0 1.0
MapReduce 1.0 1.0 1.0
Crunch 1.0 1.0 1.0
Hive 1.0 1.0 1.1
Impala 1.0 1.0 *
* depends on common HBase encoding format

Current compatibility (0.15.0)
Project HDFS (avro) HDFS (parquet) HBase
Kite 1.0 1.0 1.0
Flume Sink 1.0 1.0 1.0
MapReduce 1.0 1.0 1.0
Crunch 1.0 1.0 1.0
Hive 1.0 1.0 1.1
Impala 1.0 1.0 *
* depends on common HBase encoding format

Agenda
• Kite background
• Kite data
Application
Kite Data
Data files HBase
Maintained by the Kite

Datasets
• A collection of records or entities
• Like a Hive or relational table
• Generic, reflected, or generated objects
• Identified by URI
• dataset:hdfs:/data/ratings
• dataset:hive:/data/ratings
• dataset:hbase:zk1/ratings
ratings = Datasets.load("dataset:hive:/data/ratings")

Dataset configuration, JSON
• Schema (Avro)
• Record fields, like a table definition

• Schema (Avro)
• Partition strategy
• Layout or key definition from record fields

Configuring partitioning
[ {
"source" : "timestamp",
"type" : "year"
}, {
"type" : "month"
}, {
"type" : "day"
} ]
datasets/
└── ratings/
├── year=1997/
│ ├── month=09/
│ │ ├── day=20/
│ │ ├── ...
│ │ └── day=30/
│ ├── month=10/
│ │ ├── day=01/
│ │ ├── ...

Configuring key building
• Partition strategy for HBase
[ {
"source" : "email",
"type" : "hash",
"buckets": 32
}, {
"source" : "email",
"type" : "identity"
} ]
(22, "buzz@pixar.com")
x80x00x00x16buzz@pixar.comx00x00

• Schema (Avro)
• Layout or key definition from record fields
• Column mapping (HBase)
• Where to store record fields

{
"type" : "record",
"name" : "User",
"fields" : [ {
"name" : "email",
"type" : "string"
}, ... ]
}
Mapping example
family name counts prefs
row key last first visits flash
buzz@pixar.com Lightyear Buzz 315 true
[
{ "source": "email",
"type": "key" },
...
]

{
"type" : "record",
"name" : "User",
"fields" : [ {
"name" : "lastName",
"type" : "string"
}, ... ]
}
Mapping example
family name counts prefs
row key last first visits flash
buzz@pixar.com Lightyear Buzz 315 true
[
{ "source": "lastName",
"type": "column",
"family": "name",
"qualifier": "last" },
...
]

Command-line demo?
1. Describe your data
dataset obj-schema org.movielens.Rating --jar app.jar
--output rating.avsc
2. Describe your layout
dataset partition-config ts:year ts:month ts:day
--schema rating.avsc --output ymd.json
3. Create a dataset
dataset create ratings --schema rating.avsc
--partition-by ymd.json

Command-line tool
• Executable jar download
• Inspects the environment
• Must be used on-cluster
• Classpath for HBase, Hive, etc.
• Debugging:
debug=true ./dataset -v <command>
• Requires MAPRED_HOME variable on CDH5

Questions
Ryan Blue: blue@cloudera.com
Kite mailing list: cdk-dev@cloudera.org

Maven parent POM
• Automatic Kite and Hadoop dependencies
• Inherit from kite-app-parent-cdh4
• CDH4 only, CDH5 support in 0.16.0
<parent>
<groupId>org.kitesdk</groupId>
<artifactId>kite-app-parent-cdh4</artifactId>
<version>0.15.0</version>
</parent>

Maven Plugin
• Maven plugin manages datasets for an application
• Configured by app-parent POM
• Handles create, update, etc. in maven goals

MapReduce
• DatasetKeyInputFormat
• DatasetKeyOutputFormat
• Values are always null
View eventsBeforeToday = Datasets
.load("dataset:hive:/data/events")
.toBefore("timestamp", startOfToday());
DatasetKeyInputFormat.configure(mrJob).readFrom(eventsBeforeToday);

Crunch
• CrunchDatasets.asSource
• CrunchDatasets.asTarget
PCollection<Event> getPipeline().read(
CrunchDatasets.asSource(eventsBeforeToday);
• Handle-existing support in 0.16.0
• Configure dependencies with Kite parent POM

DatasetSink
• Write to HDFS Avro and HBase
• http://tiny.cloudera.com/DatasetSink
• Proxy user support
• Automatic partitioning
agent.sinks.name.type = org.apache.flume.sink.kite.DatasetSink
agent.sinks.name.kite.repo.uri = repo:hdfs:/datasets
agent.sinks.name.kite.dataset.name = events
agent.sinks.name.auth.proxyUser = cloudera

Kite SDK introduction for Portland Big Data

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