Updated May 11, 2023
Difference Between Hive vs Impala
Hive is a data warehouse software project built on top of Apache Hadoop developed by Jeff’s team at Facebook with a current stable version of 2.3.0 released. It is used for summarising Big data and makes querying and analysis easy. Apache Hive is an adequate standard for SQL in Hadoop. Impala is a parallel processing SQL query engine that runs on Apache Hadoop and uses to process the data stored in HBase (Hadoop Database) and Hadoop Distributed File System. Impala is an open-source product for parallel processing (MPP) SQL query engine for data stored in a local system cluster running on Apache Hadoop. Apache Hive and Impala are both key parts of the Hadoop system.
Hive
- Apache Hive helps analyze the huge dataset stored in the Hadoop (HDFS) and other compatible file systems.
- Hive QL – For querying data stored in Hadoop Cluster.
- Exploits the Scalability of Hadoop by translation.
- Hive is NOT a Full Database.
- It does not provide record-level updates.
- Hadoop is Batch Oriented System.
- Hive Queries have high latency due to MapReduce.
- Hive does not provide features. It is close to OLAP.
- Best suited for Data Warehouse Applications.
- Query execution via MapReduce.
- Query language can be used with custom scalar functions (UDF’s), aggregations (UDAF’s), and table functions (UDTF’s).
- Hive also provides Indexing to accelerate index types, including compaction and bitmap index. As of 0.10, more index types are planned.
- Storage types supported by Hive are RCfile, HBase, ORC, and Plain text.
- SQL-like queries (Hive QL) are implicitly converted into MapReduce, Tez, or Spark jobs.
- By default, Hive stores metadata in an embedded Apache Derby database.
Impala
- Impala is a query engine that runs on Hadoop. Its public beta test distribution was announced in October 2012 and became generally available on May 2013.
- It supports HDFS Apache HBase storage and Amazon S3.
- Reads Hadoop file formats, including text, Parquet, Avro, RCFile, LZO, and Sequence files.
- Supports Hadoop Security (Kerberos authentication).
- Uses metadata, ODBC driver, and SQL syntax from Apache Hive.
It supports multiple compression codecs:
1. Snappy (Recommended for its effective balance between compression ratio and decompression speed)
2. Gzip (Recommended when achieving the highest level of compression)
3. Deflate (not supported for text files), Bzip2, LZO (for text files only)
- It lets you query nested structures, including maps, structs, and arrays.
- It allows multi-user concurrent queries and also provides admission control based on prioritization and queuing of queries.
Head-to-Head Comparisons Between Hive vs Impala (Infographics)
Below are the top 20 comparisons between Hive vs Impala:
Key Difference Between Hive vs Impala
The differences between Hive vs Impala are explained in the points presented below:
- Hive was developed by Jeff’s team at Facebook, but Apache Software Foundation developed Impala.
- Hive supports the Optimized row columnar (ORC) format’s file format with Zlib compression, but Impala supports the Parquet format with snappy compression.
- Hive is written in Java, but Impala is written in C++.
- Query processing speed in Hive is slow, but Impala is 6-69 times faster than Hive.
- In Hive, Latency is high, but in Impala, Latency is low.
- Hive supports RC files and ORC storage, but Impala storage supports Hadoop and Apache HBase.
- Hive generates query expressions at compile time, but in Impala, code generation for “big loops” happens during runtime.
- Hive does not support parallel processing, but Impala supports parallel processing.
- Hive supports MapReduce, but Impala does not support MapReduce.
- There is no security feature in Hive, but Impala supports Kerberos Authentication.
- In an upgrade of any project where compatibility and speed are essential, Hive is an ideal choice but for a new project; Impala is a perfect choice.
- Hive is Fault-tolerant, but Impala does not support fault tolerance.
- Hive supports complex types, but Impala does not support difficult types.
- Hive is a batch-based Hadoop MapReduce, but Impala is an MPP database.
- Hive does not support interactive computing, but Impala supports interactive computing.
- Hive query has a “cold start problem,” but in Impala, daemon processes are started at boot time itself.
- The hive resource manager is YARN (Yet Another Resource Negotiator), but in Impala, the resource manager is native *YARN.
- Hive Distributions are all Hadoop distribution, Hortonworks (Tez, LLAP), but Cloudera MapR (*Amazon EMR) in Impala distribution.
- Hive’s audience is Data Engineers, but Impala’s is Data analysts/Data scientists.
- Hive throughput is high, but in Impala, throughput is low.
Hive vs Impala Comparison Table
The comparison between Hive vs Impala is discussed below.
| Serial No | Basis For Comparison | Hive | Impala |
| 1 | Developed By | Apache Software Foundation |
|
| 2 | File Format |
|
|
| 3 | Language | Written in Java | Written in C++ |
| 4 | Processing Speed | Hive is Slow | Impala is Fast |
| 5 | Latency | High | Low |
| 6 | Storage Support | RC file, ORC | Hadoop, Apache HBase |
| 7 | Code Conversion | Generates query expression at compile time. | Code generation happens at runtime. |
| 8 | Supports Parallel Processing | No | Yes |
| 9 | MapReduce Support | Yes | No |
| 10 | Hadoop Security | No | Supports Kerberos Authentication. |
| 11 | Usage | Ideal for project up-gradation. | Ideal for starting New Project. |
| 12 | Fault-Tolerant | Hive is Fault Tolerant. | Does not Supports Fault tolerance. |
| 13 | Complex Types | Hive supports complex types. | Impala does not support complex types. |
| 14 | Database Type | Hive is a batch-based Hadoop MapReduce. | It is an MPP database. |
| 15 | Interactive Computing | Does not support Interactive computing. | Supports Interactive Computing. |
| 16 | Execution | The hive query has a problem with “Cold Start”. | The impala process always starts at the Boot-time of Daemons. |
| 17 | Resource Management | YARN | Native *YARN |
| 18 | Distributions | HIVE – all Hadoop Distributions, Hortonworks (Tez, LLAP). | Cloudera MapR, (*Amazon EMR). |
| 19 | Audience | Data Engineers | Data Analyst/Data Scientists |
| 20 | Throughput | High Throughput | Low Throughput |
Conclusion
In this article, we have tried to showcase the two technologies, namely Hive vs Impala, and the fundamental difference between these technologies. In practical terms, we can say that Hive and Impala are not competitors. They both belong to the same foundation, which is known as MapReduce, for executing the queries; the usage of both may create a difference. According to our needs, we can use it together or the best according to the compatibility, need, and performance. The hive query language is Hive QL, a versatile and universal language. At the same time, Impala is memory intensive and does not work well for processing heavy data operations example, joining queries. If your project is related to batch processing for a large amount of data, the Hive will be better in that case, and if your work is associated with the real-time process of an ad-hoc query on data, then Impala will be better in that case.
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