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https://issues.apache.org/jira/browse/CASSANDRA-1101?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Karthick Sankarachary updated CASSANDRA-1101:
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Attachment: CASSANDRA-1101-V5.patch
CASSANDRA-1101.patch
> A Hadoop Output Format That Targets Cassandra
> ---------------------------------------------
>
> Key: CASSANDRA-1101
> URL: https://issues.apache.org/jira/browse/CASSANDRA-1101
> Project: Cassandra
> Issue Type: New Feature
> Components: Hadoop
> Reporter: Karthick Sankarachary
> Fix For: 0.7
>
> Attachments: 1101-clock-fix.diff, CASSANDRA-1101-V1.patch,
> CASSANDRA-1101-V2.patch, CASSANDRA-1101-V3.patch, CASSANDRA-1101-V4.patch,
> CASSANDRA-1101-V5.patch, CASSANDRA-1101.patch
>
>
> Currently, there exists a Hadoop-specific input format (viz.,
> ColumnFamilyInputFormat) that allows one to iterate over the rows in a given
> Cassandra column family and treat it as the input to a Hadoop map task. By
> the same token, one may need to feed the output of a Hadoop reduce task into
> a Cassandra column family, for which no mechanism exists today. This calls
> for the definition of a Hadoop-specific output format which accepts a pair of
> key and columns, and writes it out to a given column family.
> Here, we describe an output format known as ColumnFamilyOutputFormat, which
> allows reduce tasks to persist keys and their associated columns as Cassandra
> rows in a given column family. By default, it prevents overwriting existing
> rows in the column family, by ensuring at initialization time that it
> contains no rows in the given slice predicate. For the sake of speed, it
> employs a lazy write-back caching mechanism, where its record writer batches
> mutations created based on the reduce's inputs (in a task-specific map) but
> stops short of actually mutating the rows. The latter responsibility falls on
> its output committer, which makes the changes official by sending a batch
> mutate request to Cassandra.
> The record writer, which is called ColumnFamilyRecordWriter, maps the input
> <key, value> pairs to a Cassandra column family. In particular, it creates
> mutations for each column in the value, which it then associates with the
> key, and in turn the responsible endpoint. Note that, given that round trips
> to the server are fairly expensive, it merely batches the mutations
> in-memory, and leaves it on the output committer to send the batched
> mutations to the server. Furthermore, the writer groups the mutations by the
> endpoint responsible for the rows being affected. This allows the output
> committer to execute the mutations in parallel, on an endpoint-by-endpoint
> basis.
> The output committer, which is called ColumnFamilyOutputCommitter, traverses
> the mutations collected by the record writer, and sends them to the endpoints
> responsible for them. Since the total set of mutations is partitioned by
> their endpoints, each of which can be performed in parallel, it allows us to
> commit the mutations using multiple threads, one per endpoint. As a result,
> it reduces the time it takes to propagate the mutations to the server
> considering that (a) the client eliminates one network hop that the server
> would otherwise have had to make and (b) each endpoint node has to deal with
> but a sub-set of the total set of mutations.
> For convenience, we also define a default reduce task, called
> ColumnFamilyOutputReducer, which collects the columns in the input value and
> maps them to a data structure expected by Cassandra. By default, it assumes
> the input value to be in the form of a ColumnWritable, which denotes a name
> value pair corresponding to a certain column. This reduce task is in turn
> used by the attached test case, which maps every <key, value> pair in a
> sample input sequence file to a <key, column> pair, and then reduces them by
> aggregating columns corresponding to the same key. Eventually, the batched
> <key, columns> pairs are written to the column family associated with the
> output format.
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