Intermediate Tuple-based serialization

By using Tuples instead of (key, value) pairs, the user is not forced to write their custom data types (e.g. Writables) or use external serialization libraries when working with more than two fields.

However Pangool’s Tuples may contain arbitrary data types using custom serialization.

Efficient, easy-to-use secondary sorting

In Pangool you can say groupBy(“user”, “country”), sortBy(“user”, “country”, “name”). Pangool will use an intelligent and efficient Partitioner, Sort and Group Comparator underneath just like an advanced user would do with the plain Hadoop MapReduce API.

Efficient, easy-to-use reduce-side joins

Doing reduce-side joins with Pangool is as simple as it can get. By using Tuples and configuring your MapReduce jobs properly, you can easily join various datasets and perform arbitrary business logic on them. Again, Pangool will know how to partition, sort and group by underneath in an efficient way.

Instance-based configuration

Mapper, Combiner, Reducers, Input / Output Formats and Comparators can be passed via object instance. Pangool will serialize the instance into the DistributedCache and reinstantiate the object when needed. This way, boilerplate configuration code is no longer needed.

First-class multiple inputs / outputs

Multiple inputs & outputs in Pangool is part of its standard API.

Input / Output Tuple formats

Tuples may be persisted and used as input to other Jobs by using TupleOutputFormat / TupleInputFormat.

Performance and flexibility

Pangool is an alternative to the Java Hadoop MapReduce API. The same things can be achieved by using one or another. Pangool’s performance is quite close to that of Hadoop’s MapReduce API (see our benchmark with other tools for a reference). Pangool just makes life easier to those that require the efficiency and flexibility of the plain Java Hadoop MapReduce API.