Cascading 4.0 User Guide - Pipe Assemblies

The Each and Every pipes perform operations on tuple data — for instance, perform a search-and-replace on tuple contents, filter out some of the tuples based on their contents, or count the number of tuples in a stream that share a common field value.

Here is the syntax for these pipes:

Both types take the following arguments on the constructor:

The key difference between Each and Every is that an Each pipe operates on individual tuples, and an Every pipe operates on groups of tuples sent out by GroupBy or CoGroup pipes.

An Each pipe applies operations that are subclasses of the Function and Filter classes (described in the Javadoc). For example, using Each you can parse lines from a log file into their constituent fields, filter out all lines except the HTTP GET requests, and replace the "time string" fields with date fields.

Similarly, since the Every pipe works on tuple groups (the output of a GroupBy or CoGroup pipe), it applies operations that are subclasses of Aggregators and Buffers. For example, you could use GroupBy to group the output of the above Each pipe by date, then use an Every pipe to count the "GET" requests per date. The pipe would then emit the operation results as the date and count for each group.

In the syntax shown at the start of this section, the argument selector specifies fields from the input tuple to use as input values. If the argument selector is not specified, the whole input tuple (Fields.ALL) is passed to the operation as a set of argument values.

Most Operation subclasses declare result fields (shown as "declared fields" in the diagram). The output selector specifies the fields of the output Tuple from the fields of the input Tuple and the operation result. This new output Tuple becomes the input Tuple to the next pipe in the pipe assembly. If the output selector is Fields.ALL, the output is the input Tuple plus the operation result, merged into a single Tuple.

Note that it is possible for a Function, Aggregator, or Buffer to return more than one output Tuple per input Tuple. In this case, the input tuple is duplicated as many times as necessary to create the necessary output tuples. This is similar to the reiteration of values that happens during a join. If a function is designed to always emit three result tuples for every input tuple, each of the three outgoing tuples will consist of the selected input tuple values plus one of the three sets of function result values.

If the result selector is not specified for an Each pipe performing a Functions operation, the operation results are returned by default (Fields.RESULTS), discarding the input tuple values in the tuple stream. (This is not true of Filters , which either discard the input tuple or return it intact, and thus do not use an output selector.)

For the Every pipe, the Aggregator results are appended to the input Tuple (Fields.ALL) by default.

Note that the Every pipe associates Aggregator results with the current group Tuple (the unique keys that define the group). For example, if you are grouping on the field "department" and counting the number of "names" grouped by that department, the resulting output Fields will be ["department","num_employees"].

If you are also adding up the salaries associated with each "name" in each "department", the output Fields will be ["department","num_employees","total_salaries"].

This is only true for chains of Aggregator Operations — you are not allowed to chain Buffer operations, as explained below.

When the Every pipe is used with a Buffer operation, instead of an Aggregator, the behavior is different. Instead of being associated with the current grouping tuple, the operation results are associated with the current values tuple. This is analogous to how an Each pipe works with a Function. This approach may seem slightly unintuitive, but provides much more flexibility.

To put it another way, the results of the buffer operation appends the current keys that define the group only if appending the keys is relevant. It is also possible for a Buffer to return more than one result Tuple per unique grouping. A Buffer may or may not emulate an Aggregator in cases where an Aggregator is just a special optimized case of a Buffer.

The Merge pipe is very simple. It accepts two or more streams that have the same fields, and emits a single stream containing all the tuples from all the input streams. Thus a merge is just a mingling of all the tuples from the input streams, as if shuffling multiple card decks into one. Note that the output of Merge is in arbitrary order.

The example above simply combines all the tuples from two existing streams ("lhs" and "rhs") into a new tuple stream ("merge").

GroupBy groups the tuples of a stream based on common values in specified fields. If passed multiple streams as inputs, it performs a merge before the grouping. As with Merge, a GroupBy requires that multiple input streams share the same field structure.

The output of GroupBy is suitable for the Every pipe, which performs Aggregator and Buffer operations, such as counting, totaling, or averaging groups of tuples that have a common grouping value (e.g., the same date). By default, GroupBy performs no secondary sort, so within each group the tuples are in arbitrary order. For instance, when grouping on "lastname," the tuples [doe, john] and [doe, jane] are placed in arbitrary sequence of the same group.

The CoGroup pipe is similar to GroupBy. This pipe performs a join instead of a merge. CoGroup accepts two or more input streams and groups them on one or more specified keys. The join operation is performed on equal key values, similar to a SQL join.

The output stream contains all the fields in the input streams.

As with SQL, the join can be inner, outer, left, or right. Self-joins are permitted, as well as mixed joins (for three or more streams) and custom joins. Null fields in the input streams become corresponding null fields in the output stream.

Since the output is grouped, it is suitable for the Every pipe, which performs Aggregator and Buffer operations — such as counting, totaling, or averaging groups of tuples that have a common value (e.g., the same date).

The output stream is sorted by the natural order of the grouping fields. To control this order, at least the first groupingFields value given should be an instance of Fields containing Comparator instances for the appropriate fields. This allows fine-grained control of the sort grouping order.

HashJoin performs a join (similar to a SQL join) on two or more streams, and emits a stream of tuples that contain fields from all of the input streams. With a join, the tuples in the different input streams do not typically contain the same set of fields.

As with CoGroup, the field names must all be unique, including the names of the key fields, to avoid duplicate field names in the emitted Tuple. If necessary, use the declaredFields argument to specify unique field names for the output.

An inner join is performed by default, but you can choose inner, outer, left, right, or mixed (three or more streams). Self-joins are permitted. Developers can also create custom Joiners if desired. For more information on types of joins, refer to The Joiner class or the Javadoc.

The example above performs an inner join on two streams ("lhs" and "rhs"), based on common values in two fields. The field names that are specified in lhsFields and rhsFields are among the field names previously declared for the two input streams.