What is MapReduce framework name?
hadoop mapreduce hadoop-yarn hadoop2 mrv2. I am learning Hadoop and came to know that that there are two versions of the framework viz: Hadoop1 and Hadoop2.
What are the basic concepts of the MapReduce framework?
MapReduce is a software framework for processing (large1) data sets in a distributed fashion over a several machines. The core idea behind MapReduce is mapping your data set into a collection of pairs, and then reducing over all pairs with the same key.
What are the key-value pairs in MapReduce framework?
Framework specifies key-value pair in 4 places: Map input/output, Reduce input/output.
- Map Input. Map Input by default takes the line offset as the key.
- Map Output. The Map is responsible to filter the data.
- Reduce Input. Map output is input to reduce.
- Reduce Output. It totally depends on the required output.
What are main components of MapReduce?
Generally, MapReduce consists of two (sometimes three) phases: i.e. Mapping, Combining (optional) and Reducing.
- Mapping phase: Filters and prepares the input for the next phase that may be Combining or Reducing.
- Reduction phase: Takes care of the aggregation and compilation of the final result.
What is full form of HDFS?
The Hadoop Distributed File System ( HDFS ) is a distributed file system designed to run on commodity hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets.
What are the five workflow that MapReduce has?
The whole process goes through four phases of execution namely, splitting, mapping, shuffling, and reducing. This phase consumes the output of Mapping phase. Its task is to consolidate the relevant records from Mapping phase output.
What is a key value pair example?
A key-value pair is the fundamental data structure of a key-value store or key-value database, but key-value pairs have existed outside of software for much longer. A telephone directory is a good example, where the key is the person or business name, and the value is the phone number.
Why do we convert data into key value pairs?
This is because mapreduce is mainly used for data analysis and it is very easy to analyse the data when we convert it into key value pairs.In Hadoop, when the schema is static we can directly work on the column instead of keys and values, but, when the schema is not static, then we will work on keys and values.
What is combiner and partitioner in MapReduce?
The difference between a partitioner and a combiner is that the partitioner divides the data according to the number of reducers so that all the data in a single partition gets executed by a single reducer. However, the combiner functions similar to the reducer and processes the data in each partition.
What is difference between HBase and HDFS?
Instead, it is used to write/read data from Hadoop in real-time. Both HDFS and HBase are capable of processing structured, semi-structured as well as un-structured data….HDFS vs. HBase : All you need to know.
| HDFS | HBase |
|---|---|
| HDFS is a Java-based file system utilized for storing large data sets. | HBase is a Java based Not Only SQL database |
What is Spark and Kafka?
Kafka is a potential messaging and integration platform for Spark streaming. Once the data is processed, Spark Streaming could be publishing results into yet another Kafka topic or store in HDFS, databases or dashboards.
How to build a distinct set of Records in MapReduce?
The maximum work for building a distinct set of values is handled by the MapReduce framework. Each reducer is given a unique key and a set of values. We are iterating over the values and outputting the value only once and we break out the loop so we can get distinct set of records. A combiner can and should be used in the distinct pattern.
What is the function of the MAP framework in MapReduce?
The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
What is the second step of reducing in MapReduce?
The second step of reducing takes the output derived from the mapping process and combines the data tuples into a smaller set of tuples. MapReduce is a hugely parallel processing framework that can be easily scaled over massive amounts of commodity hardware to meet the increased need for processing larger amounts of data.
What is Hadoop MapReduce?
Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.