Hive and Impala in Big Data-Big Data - (PART- 4)
In this article, we will discuss Hive and Impala
Hive and Impala
Hive and Impala provide an SQL-like interface for users to extract data from Hadoop system. They reside on top of Hadoop and can be used to query data from underlying storage components.
Hive and Impala: Similarities
Hive and Impala are similar in the following ways:
- More productive than writing MapReduce or Spark directly.
- Offers interoperability with other systems.
- Brings large-scale data analysis to a broader audience.
Hive and Impala: Differences
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Hive and Impala - Comparison
Hive
- Hive is highly extensible.
- It provides more features than Impala.
- It is used mostly for batch processing.
Impala
- Impala is used mainly for interactive queries and data analysis.
- It accommodates many concurrent users.
- It comprises a specialized SQL engine that offers 5 to 50 times faster performance than Hive.
Relational Database s vs Hive vs Impala
| Features | Relational Databases | Hive | Impala |
| Query Language | SQL(Full) | SQL(subset) | SQL(subset) |
| Update individual records | YES | NO | NO |
| Delete individual records | YES | NO | NO |
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Transactions |
YES | NO | NO |
| Index Supports | Extensive | Limited | NO |
| Latency | Low | High | Average |
| Data Size | TB | PB | PB |
Hive and Impala are commonly used to analyze social media coverage.
Executing a query in Hive and Impala
Hive
- Parse HQL.
- Make Optimizations.
- Plan execution.
- Submit job(s) to the cluster.
- Monitor progress.
- Process data using MapReduce or Apache Spark.
- Store the data in HDFS.
Impala
- Parse Impala SQL.
- Make Optimizations.
- Plan execution.
- Execute query on the cluster.
- Store the data in HDFS.
Conclusion
- Hive and Impala are tools to perform SQL queries on data residing on HDFS or Hbase.
- Hive and Impala can solve the Big Data problems but cannot replace a traditional RDBMS.
- Hive runs MapReduce or Spark jobs on Hadoop based on HQL statements.
- Impala uses a very fast specialized SQL engine that is faster than MapReduce.
