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What are the optimization techniques in Hadoop?

by Rhyley Bryan

What are the optimization techniques in Hadoop?

Below are some MapReduce job optimization techniques that would help you in optimizing MapReduce job performance.

  1. Proper configuration of your cluster.
  2. LZO compression usage.
  3. Proper tuning of the number of MapReduce tasks.
  4. Combiner between Mapper and Reducer.
  5. Usage of most appropriate and compact writable type for data.

What are the optimization techniques used in hive?

Types of Query Optimization Techniques in Hive

  • a. Tez-Execution Engine in Hive.
  • b. Usage of Suitable File Format in Hive.
  • c. Hive Partitioning.
  • d. Bucketing in Hive.
  • e. Vectorization In Hive.
  • f. Cost-Based Optimization in Hive (CBO)
  • g. Hive Indexing.

What are the optimization techniques in spark?

8 Performance Optimization Techniques Using Spark

  • Serialization. Serialization plays an important role in the performance for any distributed application.
  • API selection.
  • Advance Variable.
  • Cache and Persist.
  • ByKey Operation.
  • File Format selection.
  • Garbage Collection Tuning.
  • Level of Parallelism.

What is optimization in Map Reduce?

Optimizations: Combining Phase Performance can be increased by running a mini reduce phase. on local map output. Executed on mapper nodes after map phase. Saves bandwidth before sending data to a full reducer.

What file formats can you use in Hadoop?

Formats for Hadoop

  • Text/CSV. A plain text file or CSV is the most common format both outside and within the Hadoop ecosystem.
  • SequenceFile. The SequenceFile format stores the data in binary format.
  • Avro. Avro is a row-based storage format.
  • Parquet.
  • RCFile (Record Columnar File)
  • ORC (Optimized Row Columnar)

What is cap in Hadoop?

cap theorem states that any database system can only attain two out of following states which is consistency, availability and partition tolerance. partition tolerance: the database system could be stored based on distributed architecture such as hadoop (hdfs).

How do I optimize group by query in Hive?

Best Practices to Optimize Hive Query Performance

  1. Use Column Names instead of * in SELECT Clause.
  2. Use SORT BY instead of ORDER BY Clause.
  3. Use Hive Cost Based Optimizer (CBO) and Update Stats.
  4. Hive Command to Enable CBO.
  5. Use WHERE instead of HAVING to Define Filters on non-aggregate Columns.

Which join is faster in Hive?

Since map join is faster than the common join, it’s better to run the map join whenever possible. Previously, Hive users needed to give a hint in the query to specify the small table. For example, select /*+mapjoin(a)*/ * from src1 x join src2 y on x. key=y.

What are the optimization techniques?

Preface.

  • Linear Programming. Interior point methods for semi-infinite programming calculations.
  • Nonlinear Programming. Numerical experience with limited-memory quasi-Newton and truncated Newton methods.
  • Combinatorial Optimization.
  • Network Optimization.
  • Multi-Criteria Optimization.
  • Optimal Control.
  • Large Scale Optimization.

    • How do you optimize PySpark codes?

    PySpark execution logic and code optimization

    1. DataFrames in pandas as a PySpark prerequisite.
    2. PySpark DataFrames and their execution logic.
    3. Consider caching to speed up PySpark.
    4. Use small scripts and multiple environments in PySpark.
    5. Favor DataFrame over RDD with structured data.
    6. Avoid User Defined Functions in PySpark.

    What is job optimization?

    Optimization pushes processing functionality and related data I/O into database sources or targets or Hadoop clusters, depending on the optimization options that you choose. When you optimize a job, Balanced Optimization searches the job for patterns of stages, links, and property settings.

    What is the output of reducer?

    In Hadoop, Reducer takes the output of the Mapper (intermediate key-value pair) process each of them to generate the output. The output of the reducer is the final output, which is stored in HDFS. Usually, in the Hadoop Reducer, we do aggregation or summation sort of computation.

    Which is the best book for Hadoop for Dummies?

    Hadoop for Dummies by Dirk Deroos This Hadoop book is easy to read and understand. It makes readers understand the value of Big data and covers concepts like origin of Hadoop . its functionality and benefits and few Big Data practical applications.

    Are there any job optimization techniques in Hadoop?

    Conclusion In conclusion of the Hadoop Optimization tutorial, we can say that there are various Job optimization techniques that help you in Job optimizing in MapReduce. Like using combiner between mapper and Reducer, by LZO compression usage, proper tuning of the number of MapReduce tasks, Reusage of writable.

    How is tail used in Hadoop for Dummies?

    Usage: hdfs dfs -stat URI [URI …] Example: hdfs dfs -stat /user/hadoop/dir1. tail: Displays the last kilobyte of a specified file to stdout. The syntax supports the Unix -f option, which enables the specified file to be monitored. As new lines are added to the file by another process, tail updates the display.

    What should I look for in performance tuning for Hadoop?

    Tuning Hadoop run-time parameters. Hadoop provides a set of options on cpu, memory, disk, and network for performance tuning. Most hadoop tasks are not cpu bounded, what we usually look into is to optimize usage of memory and disk spills.