Spark and Impala are the two most common tools used for big data analytics. This article focuses on discussing the pros, cons, and differences between the two tools.
What is Spark?
Spark is a framework that is open source and is used for making queries interactive, for machine learning, and for real-time workloads. It was developed by Databricks, Apache Software Foundation, and Holden Karau in 2014. It is written in Python, Scala, Java, and R language and is available in Scala, Java, SQL, Python, R, C#, and F# languages. It has Apache License 2.0 and can run on Microsoft Windows, macOS, and Linux. Companies using Spark are 4Quant, Amazon, Art.com, Alibaba and many more.
Features of Spark
- Flexible: Spark is flexible for handling multiple data types since it can operate with a variety of data sources such as HDFS, HBase, Cassandra, and more.
- Single integrated platform: Under a single integrated platform, Spark allows batch processing, interactive searches, streaming processing of data, and machine learning.
- In-memory computing: Through the use of in-memory computing, Spark is able to keep intermediate data in memory, significantly improving processing efficiency.
Advantages of Spark
- Fault Tolerance: Data is not lost regardless of the event of node malfunctions because of Spark’s RDDs built-in fault tolerance.
- Ease of Use: Dealing with large-scale data processing tasks is relatively easy for developers due to Spark’s APIs and a high-level abstraction.
- Large Ecosystem: Spark’s wide library ecosystem offers a number of pre-built tools for a variety of tasks, reducing the amount of effort and time required for development.
Disadvantages of Spark:
- Learning Curve: When introducing more and more complex features and optimizations, Spark’s learning curve might be hard for beginners.
- Complexity: The development process is made tougher by Spark’s distributed nature, and debugging systems that are distributed can be challenging.
- Resource Management: Managing resources efficiently within a cluster, such as memory and CPU, may be difficult and calls for careful setup and monitoring.
What is Impala?
Impala is an open-source software which comes under the category of Massive Parallel Processing SQL query engine. It helps to process huge volumes of data that is stored in the Hadoop cluster. It was developed by Cloudera, Apache Software Foundation in 2013. It is written in programming languages like JAVA, C++ and has Apache License 2.0. Companies that are using Impala are Teradata, Apache HBase, Apache Hadoop, Informatica and many more.
Features of Impala
- Data caching: Data caching is supported by Impala, making it feasible to cache frequently accessed data in memory for easier access.
- Different file types supported: It is capable of working with a wide range of file types frequently seen in Hadoop ecosystems, including Parquet, Avro, and RCFile.
- Suitable for analytical applications: It is well-suited for interacting with data and analytical applications since it is designed for low-latency queries.
Advantages of Impala
- SQL Compatibility: Since Impala is SQL compatible, users who are familiar with SQL can quickly begin using Impala to query data without having to learn new query languages.
- Real-time Interactive Queries: It succeeds in offering quick answers to active ad-hoc queries, helping users to explore data and carry out research studies right away.
- Integration: Impala’s seamless integration with the Hadoop ecosystem allows customers to take advantage of their current HDFS and Hive infrastructure.
Disadvantages of Impala
- Suitability: Impala is only suitable for SQL-based queries.
- Absence of Update and Delete Support: Impala does not directly support updates or deletions on data stored in HDFS.
- Resource Management: For optimal Impala performance, cluster assets, such as memory, must be managed effectively. Performance problems may result from configuration errors.
Spark vs Impala
|
Parameters |
Spark |
Impala |
|---|---|---|
|
Developed |
It was developed by Apache Software Foundation. |
It was developed by Cloudera. |
|
Language |
It is written in Python, Scala, Java, R language. |
It is written in JAVA, C++ language. |
|
Fault Tolerance |
Both short- and long-term queries can run in Spark. |
Only short-term queries are focused in Impala. |
|
Server-side scripts |
It does not support Server-Side scripts in it. |
It supports Server-Side Scripts. |
|
Replication |
In Spark, Replication is not possible. |
Replication is possible in only selective factors. |
|
Access Control |
There is no user concept in Spark. |
There are access rights for individuals, users, groups in Impala. |
Conclusion
Both the tools play their own parts in their respective works. However, if there are no complex functionalities needed then Impala is a great option as it does not support these kinds of functionalities like Spark. The greatest advantage of Spark is that it is fault tolerant, thus, it can handle complex functions. Both the software have its own advantages and disadvantages. The selection of the platform depends on the user after going through all the requirements in their organization.