Cascading 3.3 User Guide - Extending Cascading

1. Introduction: 1.1. What Is Cascading?

1.2. Another Perspective

1.3. Why Use Cascading?

1.4. The Cascading Philosophy

1.5. Who Are the Users?
2. Diving into the APIs: 2.1. Anatomy of a Word-Count Application

2.2. Fluid: An Alternative Fluent API
3. Cascading Basic Concepts: 3.1. Terminology

3.2. Pipe Assemblies

3.3. Pipes

3.4. Platforms

3.5. Sourcing and Sinking Data

3.6. Sink Modes

3.7. Flows
4. Tuple Fields: 4.1. Field Sets

4.2. Field Algebra

4.3. Field Typing

4.4. Type Coercion
5. Pipe Assemblies: 5.1. Each and Every Pipes

5.2. Merge

5.3. GroupBy

5.4. CoGroup

5.5. HashJoin
6. Flows: 6.1. Creating Flows from Pipe Assemblies

6.2. Configuring Flows

6.3. Skipping Flows

6.4. Creating Custom Flows

6.5. Process Levels in the Flow Hierarchy

6.6. Runtime Metrics
7. Cascades: 7.1. Creating a Cascade

7.2. The Cascade Topological Scheduler
8. Configuring: 8.1. Introduction

8.2. Creating Properties

8.3. Passing Properties
9. Local Platform: 9.1. Building an Application

9.2. Executing an Application

9.3. Source and Sink Taps

9.4. Troubleshooting and Debugging
10. The Apache Hadoop Platforms: 10.1. What is Apache Hadoop?

10.2. Hadoop 1 MapReduce vs. Hadoop 2 MapReduce

10.3. Hadoop 2 MapReduce vs Hadoop 2 Tez

10.4. Configuring Applications

10.5. Building an Application

10.6. Executing an Application

10.7. Troubleshooting and Debugging

10.8. Source and Sink Taps

10.9. Custom Taps and Schemes

10.10. Partial Aggregation instead of Combiners

10.11. Custom Types and Serialization
11. Apache Hadoop MapReduce Platform: 11.1. Configuring Applications

11.2. Creating Flows from a JobConf

11.3. Building
12. Apache Tez Platform: 12.1. Configuring Applications

12.2. Building
13. Using and Developing Operations: 13.1. Introduction

13.2. Functions

13.3. Filters

13.4. Aggregators

13.5. Buffers

13.6. Operation and BaseOperation
14. Custom Taps and Schemes: 14.1. Introduction

14.2. Custom Taps

14.3. Custom Schemes

14.4. Taps with File and Nonfile Resources

14.5. Tap Life-Cycle Methods
15. Advanced Processing: 15.1. SubAssemblies

15.2. Stream Assertions

15.3. Failure Traps

15.4. Checkpointing

15.5. Restarting a Checkpointed Flow

15.6. Flow and Cascade Event Handling

15.7. PartitionTaps

15.8. Partial Aggregation instead of Combiners
16. Built-In Operations: 16.1. Identity Function

16.2. Debug Function

16.3. Sample and Limit Functions

16.4. Insert Function

16.5. Text Functions

16.6. Regular Expression Operations

16.7. Java Expression Operations

16.8. XML Operations

16.9. Assertions

16.10. Logical Filter Operators

16.11. Buffers
17. Built-in SubAssemblies: 17.1. Optimized Aggregations

17.2. Stream Shaping
18. Cascading Best Practices: 18.1. Unit Testing

18.2. Flow Granularity

18.3. SubAssemblies, not Factories

18.4. Logical Responsibilities for SubAssemblies

18.5. Java Operators in Field Names

18.6. Debugging Planner Failures

18.7. Optimizing Joins

18.8. Debugging Streams

18.9. Handling Good and Bad Data

18.10. Maintaining State in Operations

18.11. Fields Constants

18.12. Checking the Source Code
19. Extending Cascading: 19.1. Scripting

19.2. Custom Types and Serialization

19.3. Custom Comparators and Hashing
20. Cookbook: Code Examples of Cascading Idioms: 20.1. Tuples and Fields

20.2. Stream Shaping

20.3. Common Operations

20.4. Stream Ordering

20.5. API Usage
21. The Cascading Process Planner: 21.1. FlowConnector

21.2. RuleRegistrySet

21.3. RuleRegistry

21.4. Debugging RuleRegistrySets

Extending Cascading

Scripting

The Cascading API was designed with scripting in mind. Any JVM-compatible scripting language can import and instantiate Cascading classes, create pipe assemblies and Flows, and execute those Flows. And if the scripting language in question supports domain-specific language (DSL) creation, users can create their own DSLs to handle common idioms.

The Cascading website (http://cascading.org/extensions/) includes information on scripting language bindings that are publicly available.

Custom Types and Serialization

The Tuple class is a generic container for all java.lang.Object instances.

Thus any primitive value or custom class can be stored in a Tuple instance — that is, returned by a Function, Aggregator, or Buffer as a result value.

Unfortunately there is no common method for managing the serialization of custom types that is cross-platform. See the platform-specific topics of this User Guide documentation for details about registering serializers that Cascading can adopt at runtime.

Custom Comparators and Hashing

Frequently, objects in one Tuple are compared to objects in a second Tuple. This is especially true during the sort phase of GroupBy and CoGroup. By default, Cascading uses the equals() and hashCode() Object native methods to compare two values and get a consistent hash code for a given value, respectively.

There are two different approaches that you can take to override the default behavior:

Create a java.util.Comparator class to perform comparisons on given field in a Tuple. For instance, to secondary-sort a collection of custom Person objects in a GroupBy, use the Fields.setComparator() method to designate the custom Comparator to the Fields instance that specifies the sort fields.
Alternatively, you can set a default Comparator for a Flow or for a local Pipe instance by one of the following ways:
- Either calling FlowProps.setDefaultTupleElementComparator() on a Properties instance
- Or using the cascading.flow.tuple.element.comparator property key

If the hash code must also be customized, the custom Comparator can implement the cascading.tuple.Hasher interface.

For more information, see the Javadoc.