Hadoop is a popular technology that handles petabytes in data for enterprise applications. Enterprises often work in a tight time frame and require fast analysis of the data collected over a short period. Hadoop MapReduce is a complex tool for analysis. You will also need to have some programming skills in order to explore MapReduce data.
This is where SQL comes in handy for data query language such as SQL. It can be used for data analysis, extraction, and processing. Although SQL has been tested with Hadoop data store, it is not compatible with big data front end. It is not designed for analytical purposes and is therefore a general-purpose database language. Apache Hive’s query language HQL/HiveQL can be used to query historical data. Apache Hive also gives you the opportunity to have better control over your data.
Why is Apache Hive faster than SQL?
Open source is a common theme in organizations. This led to Apache Hive being added to the Hadoop family to support the analysis query. Apache Hive is a data warehouse that is built on top of Apache Hadoop. Apache Hive in Hadoop’s primary purpose is to summarize large datasets and perform ad-hoc queries on them. You will also be able to project a structure onto the Hadoop data. Apache Hive also provides an interface for receiving data from Hadoop cluster. It is a great way for data analysis to be started faster.
Let’s take a look at the technical aspects that make Hive more efficient in processing queries.
Partitioning of Table
Hive’s dynamic and static partitioning techniques significantly improve performance optimization. Hadoop data store, also known as HDFS, stores petabytes worth of data that can be accessed by Hadoop users for data analysis. It is a heavy burden, no doubt! Hive uses the Partitioning method to divide all stored table data into multiple parts. Each partition targets a particular value of the partition column. These values are the records in the HDFS. When you query a table you actually are querying a partition.
Apache Hive converts the SQL query into a MapReduce job and submits it to Hadoop cluster. You will then get the query value. The partitioning process reduces operational I/O time as well as execution load. The overall performance of the system is thus improved.
It is possible for large table data to be too big for the partition size. In this situation, partitioning is not possible. Hive allows users to break down data sets into smaller, more manageable pieces rather than subdivisions. To subdivide partitions, Hive uses the Hash function.
Use of the TEZ Engine
Apache TEZ can be used instead of MapReduce to execute Apache Hive. TEZ, which is a framework and API for developers to create native YARN apps, provides highly optimized data processing features. Apache Hive is a faster query engine that TEZ uses with Apache Hive. This is a shared-nothing architecture, where each processing unit has its own memory and disk resources. It works independently.
ORCFILE is used
ORCFile is a new format for table storage that allows for significant speed improvements using different techniques such as predicate push-down or compression. Apache Hive supports ORCFile on every Hive table. This is extremely helpful in speeding up execution.
Vectorization is a key feature in Hive 0.13 and beyond. This feature allows batch execution of queries such as joins, scans and aggregations.
Cost-based Optimization (CBO).
Hive’s cost-based optimization method optimizes the physical and logical execution plans for each query. It uses query cost to optimize the order and types of joins for each query.
Dynamic Runtime Filtering
Apache Hive Dynamic Runtime Filtering provides a fully dynamic solution to table data. An automatic bloom filter is implemented using Dynamic Runtime Filtering. It works with actual dimension table values. The filter removes rows and skips records that don’t match. This data does not have any joins or shuffle operations. It saves significant CPU and network usage.
(Image Source: https://hortonworks.comIntroduction of Hive LLAP
LLAP (Low Latency Analysis Processing) is a second generation big data system that combines optimized in-memory cache and persistent data query. LLAP SSD Cache, which is a combination RAM and SSD, creates a huge pool of memory. It makes computations happen in memory and not on disk. It intelligently caches memory and shares the computed data.