Impala vs Hive – Difference Between Hive and Impala
Both Apache Hive and Impala, used for running queries on HDFS. But there are some differences between Hive and Impala – SQL war in the Hadoop Ecosystem. So, in this article, “Impala vs Hive” we will compare Impala vs Hive performance on the basis of different features and discuss why Impala is faster than Hive, when to use Impala vs hive. Before comparison, we will also discuss the introduction of both these technologies.
2. Introduction: Impala vs Hive
a. What is Hive?
Basically, for performing data-intensive tasks we use Hive. Such as querying, analysis, processing, and visualization. It was first developed by Facebook. Also, it is a data warehouse infrastructure build over Hadoop platform. Moreover, Hive is versatile in its usage since it supports analysis of huge datasets stored in Hadoop’s HDFS and other compatible file systems. Like Amazon S3. Hive offers an SQL – like language (HiveQL) with schema on reading and transparently converts queries to MapReduce, Apache Tez, and Spark jobs. Some of the best features of Hive are:
- Like it offers to index for accelerated processing
- Hive supports several types of storages. Such as Plain Text, RCFIle, HBase, ORC
- Also, it supports Metadata storage in RDBMS
- Hive supports SQL like queries. Though we can get implicitly converted into MapReduce, Tez or Spark jobs
- To manipulate strings, dates it has Built-in User Defined Functions (UDFs)
Learn more about Hive Architecture & Components with Hive Features in detail.
b. What is Impala?
On defining Impala we can say it is an open source Massively Parallel Processing (MPP) SQL engine. Moreover, for running queries on HDFS and Apache HBase, Impala is a wonderful choice. For processing, it doesn’t require the data to be moved or transformed prior. However, it is easily integrated with the whole of Hadoop ecosystem. Also, for open source interactive business intelligence tasks, Impala’s unified resource management across frameworks makes it the standard. Some of the best features of Impala are:
- Impala does support for Hadoop Distributed File System (HDFS) and Apache HBase
- However, Impala also recognizes Hadoop file formats like text, LZO, Avro, RCFile, Parquet
- It also Supports Kerberos authentication
- With Apache Sentry, it also offers Role based authorization.
If these professionals can make a switch to Big Data, so can you:
3. Difference between Hive and Impala
Following are the featurewise comparison between Impala vs Hive:
a. Query Process
Basically, in Hive every query has the common problem of a “cold start”.
Impala avoids any possible startup overheads, being a native query language. However, that are very frequently and commonly observed in MapReduce based jobs. Moreover, to process a query always Impala daemon processes are started at the boot time itself, making it ready.`
b. Intermediate Results
Basically, Hive materializes all intermediate results. Hence, it enables enabling better scalability and fault tolerance. However, that has an adverse effect on slowing down the data processing.
However, it’s streaming intermediate results between executors. Although, that trades off scalability as such.
Learn Comparison between Hive Internal Tables vs External Tables
c. During the Runtime
At Compile time, Hive generates query expressions.
During the Runtime, Impala generates code for “big loops”.
d. Interactive Computing
For interactive computing, Hive is not an ideal.
For interactive computing, Impala is meant.
Basically, it is a batch based Hadoop MapReduce
However, it is more like MPP database
f. Complex Types
Though, it supports complex types
However, it does not support complex types
Must Know- Important Difference between Hive Partitioning vs Bucketing
g. Query Execution
The output of the query will be produced as Hive is fault tolerant, while a data node goes down during the query execution.
Impala starts all over again, while a data node goes down during the query execution.
while keeping Hive’s ability to perform well at mid to high query complexity, Hive LLAP gets good performance at the low end.
Similarly, while Impala struggles as query complexity increases but Impala perform well with less complex queries.
i. SQL Queries
Hive LLAP allows customers to perform sub-second interactive queries without the need for additional SQL-based analytical tools.
Impala offers fast, interactive SQL queries directly on our Apache Hadoop data stored in HDFS or HBase.
j. Time consumption
The dynamic runtime features of Hive LLAP minimizes the overall work. Hence, we can say working with Hive LLAP consumes less time.
Impala consumes less time for simpler queries, but for complex queries, it needs more time than Hive LLAP.
k. Direct interaction
Hive LLAP has Long-Lived Daemons. That replaces direct interaction with HDFS Data Nodes and tightly integrated DAG-based framework.
Impala needs to have the file in Apache Hadoop HDFS storage or HBase (Columnar database).
l. ETL jobs
For long running ETL jobs, Hive is an ideal choice, since Hive transforms SQL queries into Apache Spark or Hadoop jobs.
However, Impala is 6-69 times faster than Hive.
Let’s learn Hive Data Types Tutorial with Example
n. When to use
The hive will be your ideal choice, if you are considering of taking up an upgradation project then compatibility comes up as an important factor to rely upon.
Impala is the best choice out of the two if you are starting something fresh.
So, this was all in Impala vs Hive. Hope you likeour explanation.
4. Conclusion – Impala vs Hive
As a result, we have learned about both of these technologies. Apache Hive and Impala. Also, we have covered details about this Impala vs Hive technology in depth. However, we have shown few differences between Hive and Impala technology but in practice, these are not two different competitors competing to show which one of them is the best. Although, each complements other in rarely good use cases each of them is known for their characteristics as defined earlier.
But practically we can say both of Apache Hive and Impala need not be competitors competing with each other. Well, to execute queries both Hive and Impala has a strong MapReduce foundation. However, when we need to use both together, we get the best out of both the worlds. Such as compatibility and performance. Well, after learning Impala vs Hive, still if any query occurs feel free to ask in the comment section.
Related Topic- Hive Operators & HBase vs Hive