However, the reduce input must have the same types as the map output, although the reduce output types may be different again (K3 and V3). The map and reduce functions in Hadoop MapReduce have the following general form: map: (K1, V1) → list(K2, V2) reduce: (K2, list(V2)) → list(K3, V3) In general, the map input key and value types (K1 and V1) are different from the map output types (K2 and V2). This chapter looks at the MapReduce model in detail, and in particular at how data in various formats, from simple text to structured binary objects, can be used with this model. MapReduce has a simple model of data processing: inputs and outputs for the map and reduce functions are key-value pairs. Flume configuration for second-tier agent in a load balancing scenario The configuration for one of the second-tier agents, agent2a, is shown in Example 14-6.A Flume configuration for load balancing between two Avro endpoints using a sink group A two-tier Flume configuration using a spooling directory source and an HDFS sink Flume configuration using a spooling directory source, fanning out to an HDFS sink and a logger sink Flume configuration using a spooling directory source and an HDFS sink Flume configuration using a spooling directory source and a logger sink MapReduce program to convert text files to Parquet files using AvroParquetOutputFormat A MapReduce program to sort an Avro datafile MapReduce program to find the maximum temperature, creating Avro output A Python program for writing Avro record pairs to a datafile In-Memory Serialization and Deserialization.
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