Apache Hive Optimization Techniques — 1.
- Partitioning.
- Bucketing.
- Using Tez as Execution Engine.
- Using Compression.
- Using ORC Format.
- Join Optimizations.
- Cost-based Optimizer.
Full Answer
What are the hive optimization techniques for hive performance tuning?
Following are the Hive optimization techniques for Hive Performance Tuning, let’s discuss them one by one: a. Tez-Execution Engine in Hive Tez Execution Engine – Hive Optimization Techniques, to increase the Hive performance of our hive query by using our execution engine as Tez.
What is the best way to optimize hive indexing?
Hive Index – Hive Optimization Techniques, one of the best ways is Indexing. To increase your query performance indexing will definitely help. Basically, for the original table use of indexing will create a separate called index table which acts as a reference.
What is cost-based optimization in hive?
Cost-Based Optimization in Hive (CBO) Cost-Based Optimization in Hive – Hive Optimization Techniques, before submitting for final execution Hive optimizes each Query’s logical and physical execution plan. Although, until now these optimizations are not based on the cost of the query.
Why is hive query optimization important?
If the Hive code is not written properly, you may face timing in hive query execution. And so hive performance tuning is very important. When you do Hive query optimization, it helps the query to execute at least by 50%. If your query is not optimized, a simple select statement can take very long to execute.
What are the optimization techniques used in Hive?
Types of Query Optimization Techniques in Hivea. 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 is query optimization in Hive?
Apache Hive is a query and analysis engine which is built on top of Apache Hadoop and uses MapReduce Programming Model. It provides an abstraction layer to query big-data using the SQL syntax by implementing traditional SQL queries using the Java API.
How do I optimize Hive joins?
Physical Optimizations:Partition Pruning.Scan pruning based on partitions and bucketing.Scan pruning if a query is based on sampling.Apply Group By on the map side in some cases.Optimize Union so that union can be performed on map side only.Decide which table to stream last, based on user hint, in a multiway join.More items...•
How does CBO work in Hive?
The CBO, powered by Apache Calcite, is a core component in the Hive query processing engine. The CBO optimizes plans for executing a query, calculates the cost, and selects the least expensive plan to use. In addition to increasing the efficiency of execution plans, the CBO conserves resources.
What are optimization techniques for Hadoop?
1) Memory Tuning.2) Improving IO Performance.3) Minimizing the Disk Spill by Compressing Map Output.4) Tuning the Number of Mapper or Reducer Tasks.5) Writing a Combiner.6) Using Skewed Joins.7) Speculative Execution.
Why is partition technique used in Hive?
The partitioning in Hive means dividing the table into some parts based on the values of a particular column like date, course, city or country. The advantage of partitioning is that since the data is stored in slices, the query response time becomes faster.
What are the optimization techniques in spark?
8 Performance Optimization Techniques Using SparkSerialization. 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 cost based optimization in Hive?
Hive's Cost-Based Optimizer (CBO) is a core component in Hive's query processing engine. Powered by Apache Calcite, the CBO optimizes and calculates the cost of various plans for a query.
What is indexing in Hive?
Introduction to Indexes in Hive. Indexes are a pointer or reference to a record in a table as in relational databases. Indexing is a relatively new feature in Hive. In Hive, the index table is different than the main table. Indexes facilitate in making query execution or search operation faster.
What is vectorization in Hive?
Vectorization allows Hive to process a batch of rows together instead of processing one row at a time. Each batch is usually an array of primitive types. Operations are performed on the entire column vector, which improves the instruction pipelines and cache usage.
What is Hive architecture?
Architecture of Hive Hive is a data warehouse infrastructure software that can create interaction between user and HDFS. The user interfaces that Hive supports are Hive Web UI, Hive command line, and Hive HD Insight (In Windows server).
What are the components of a hive?
The main components of the Hive are as follows: While Hadoop/hive can process nearly any amount o f data, but optimizations can lead to big savings, proportional to the amount of data, in terms of processing time and cost. There are a whole lot of optimizations that can be applied in the hive.
What is Apache Hive?
Apache Hive is a query and analysis engine which is built on top of Apache Hadoop and uses MapReduce Programming Model. It provides an abstraction layer to query big-data using the SQL syntax by implementing traditional SQL queries using the Java API. The main components of the Hive are as follows:
What compression algorithm can be changed using Orc.compress?
Achieves higher level of compression, the compression algorithm can be changed using orc.compress setting in the hive. By default, it uses Zlib Compression. It has the ability to skip scanning an entire range of rows within a block, if irrelevant to the query, using the light-weight indexes stored within the file.
What is a hash table in Star Schema?
Useful for star schema joins, this joining algorithm keeps all of the small tables (dimension tables) in memory in all of the mappers and big table (fact table) is streamed over it in the mapper. This avoids shuffling cost that is inherent in Common-Join. For each of the small table (dimension table), a hash table would be created using join key as the hash table key and when merging the data in the Mapper Function, data will be matched with the mapping hash value.
Can you use Snappy on a hive?
None: No compression at all. If the CPU is tight, you might want to use Snappy. Rarely will you want to use none, which is used primarily for diagnostic purposes. Also, the Hive’s writer is intelligent if it does not foresee any marginal gain, it will not compress the data.
How does hive work?
As we know, Hive converts the queries into different stages during execution. These stages are usually getting executed one after the other and thus increases the time of execution. Below are some of the normal steps involved-
Why is hive performance tuning important?
If the Hive code is not written properly, you may face timing in hive query execution. And so hive performance tuning is very important. When you do Hive query optimization, it helps the query to execute at least by 50%. If your query is not optimized, a simple select statement can take very long to execute.
How does vectorization improve query performance?
Vectorization improves the query performance of all the operation like scans, aggregations, filters and joins, by performing them in batches of 1024 rows at once instead of single row each time .
What is Apache Tez?
Apache TEZ is an execution engine used for faster query execution. It fastens the query execution time to around 1x-3x times. To use TEZ execution engine, you need to enable it instead of default Map-Reduce execution engine. TEZ can be enabled using the below query-. Set hive.execution.engine=tez;
What is partition in hive?
Partition is a useful concept in Hive. It is used to divide the large table based on certain column so that the whole data can be divided into small chunks. It allows you to store the data under sub-directory inside a table.
What is ORC in Hadoop?
ORC (optimized record columnar) is great when it comes to hive performance tuning. We can improve the query performance using ORC file format easily. You can check Hadoop file formats in detail here.
What is a hive performance tuning?
What is Hive performance tuning? Hive performance tuning refers to the collective processes and steps designed to improve and accelerate the performance of your Hive environments.
What is hive execution engine?
Hive is a continual work in progress. Execution engines are a clear focus for the developers as we see things like Tez, LLAP, and Hive on Spark look to add to core Hive in ways that improve performance without the need for low-level tuning. Understanding and leveraging the best execution engine for the task at hand should be a mandatory consideration for tuning Hive query performance.
What is partitioning in hive?
Partitioning is a common Hive performance tuning tactic which places table data in separate subdirectories of a table location based on keys. Partition keys present an opportunity to target a subset of the table data rather than scanning data you don’t need for your operations.
What is bucketing in hive?
Bucketing, similar to Partitioning, is a Hive query tuning tactic that allows you to target a subset of data. In this case, to improve join performance specifically by scanning less data. This improves the query across the vectors of time and efficiency as less data has to be input, output, or stored in memory.
Why does hive only read data?
No matter how much data exists, when you have partitions, Hive only reads a specific amount of data to generate results. This drastically improves performance, even when you execute complex analytics queries. This is because Hive only has to read data from a few partitions specified in the clause.
Why is input format selection important in hive?
That is because JSON, and similar format types, actually take up lots of space as well as some overhead of parsing.
Is a query as fast as the slowest task?
The overall query is only as fast as the slowest task. Ensuring an even distribution of work across the tasks is an effective Hive performance tuning approach. That’s because it keeps the query from slowing itself down by handling more data than necessary in some tasks.
