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Apache Hive
Apache Hiveis a SQL data access interface for the Apache Hadoop platform.
Hive allows you to query, aggregate, and analyze data using SQL syntax.
HiveQL queries are translated into Java code for MapReduce jobs.
These are three execution engines that can be launched on Hadoop.
Databases are arrays composed of partitions, which can again be broken down into buckets.
The data is accessible via HiveQL.
Within each database, the data is numbered, and each table corresponds to an HDFS directory.
Apache Hives central directory is a metastore containing all the information.
The engine that makes Hive work is called the driver.
It bundles a compiler and an optimizer to determine the optimal execution plan.
Finally, security is provided by Hadoop.
It, therefore, relies on Kerberos for mutual authentication between the client and server.
It makes it possible to obtain qualitative insights, providing a competitive advantage and facilitating responsiveness to market demand.
This solution is much more scalable than a traditional database.
Finally, the work capacity is unparalleled since it can perform up to 100,000 requests per hour.
In this case, parallel execution of fragments of the SQL query is possible.
Impala depends infrastructurally on another popular SQL-on-Hadoop tool, Apache Hive, using its metadata store.
In particular, the Hive Metastore lets Impala know about the availability and structure of the databases.
The connected impalad becomes the coordinator of the current request.
Impalad directly accesses HDFS and HBase using local instances of system services to provide data.
Unlike Apache Hive, such direct interaction significantly saves query execution time, as intermediate results are not saved.
In response, each daemon returns data to the coordinating impalad, sending the results back to the client.
It significantly saves query execution time.
In addition, Impala generates program code at runtime and not at compilation, as Hive does.
However, a side effect of Impalas high-speed performance is reduced reliability.
In addition, they also use the HDFS distributed file system.
Whenever possible, Impala works with an existing Apache Hive infrastructure already used to execute long-running SQL batch queries.
Hive Vs Impala: Differences
Hive is written in Java, whereas Impala is written in C++.
However, Impala also uses some Java-based Hive UDFs.
Impala executes SQL queries in real-time, while Hive is characterized by low data processing speed.
With simple SQL queries, Impala can run 6-69 times faster than Hive.
However, Hive handles complex queries better.
The throughput of Hive is significantly higher than that of Impala.
Hive is a fault-tolerant system that preserves all intermediate results.
It also positively affects scalability but leads to a decrease in data processing speed.
In turn, Impala cannot be called a fault-tolerant platform because its more memory bound.
Hive generates query expressions at compile time, while Impala generates them at runtime.
Impala does not have this kind of startup overhead.
Impala supports LZO, Avro, and Parquet formats, while Hive works with Plain Text and ORC.
However, both support the RCFIle and Sequence formats.
Final Words
Hive and Impala do not compete but rather effectively complement each other.
