Updated April 4, 2023
Difference Between Presto vs Impala
Impala and Presto (or Presto DB) are a distributed SQL query engine and a native analytic database designed from the ground up for fast analytic queries on any size of data meanwhile Both works well for BI Queries and are open Source OLAP engines respectively. The fact that Amazon Web Services and MapR have both acquired support for Impala in less than two years demonstrates its rapid ascent. Presto took over the Hive in 2012 and was faster than other engines around 20x. Presto is not a database but we could store data and is built on java to deliver ad-hoc analytic.
Head to Head Comparison Between Presto vs Impala (Infographics)
Below are the top differences between Presto and Impala:
Key Differences
Next, we shall see a few key differences between Presto and Impala.
- Apache Impala is a modern Real-time Query for HDFS and Presto is an Open-source Distributed SQL Engine and both belong to Big Data Tools.
- Impala is written in C++ and java and presto is built in java.
- The Unique Key factor of Presto was it works directly on Files with no ETL formats and Impala is considered to be a super-fast performer. Though it is considered faster presto is much more Pluggable. Impala has efficient metadata caching.
- Impala Supports HDFS for storage whereas Presto lacks it. Presto directly communicates with HDFS and has connectors with them to have communicated with data sources. Presto doesn’t have data limitations as we generate hourly or daily reports, for example, Facebook users.
- Presto concept was developed from a parallel database named Volcano and designed for high-speed data analysis. But Impala is designed for PB level real-time query analysis for the CDH platform. Impala is a good option for reducing query latency, especially for concurrent executions. Presto is used for an increased workload.
- Presto was created to make query processing in commercial data warehouses faster. It has the potential to scale the organizational size to match Facebook.
- Impala doesn’t use Hive and MapReduce but prefers relational databases. As Presto is memory-based it is found that it takes less memory when Querying compared to Impala.
- Impala is connected with Hadoop’s inherent security and Kerberos for authentication. Data Access is controlled here.LDAP authentication is done in Presto.
- The ORC, Parquet, and RCFile file formats are all supported by Presto. As a result, it’s regarded as a fantastic query engine that also eliminates the requirement for data transformation. RCFile, Parquet, Avro file, and Sequence File formats are supported by Impala.
- Presto stores intermediate results in a Buffer cache and Impala doesn’t use MapReduce to store an intermediate result instead uses In-Memory and hence gives slow processing.
Comparison Table
At all times, developers are on the search for practical and efficient SQL engines. The Impala and Presto engines have been the most popular among those available on the market. Let’s see a head-to-head comparison of Presto and Impala to see their insights and practices.
| Presto | Impala | |
| Definition | Presto is a massively parallel and distributed big data query engine designed from the bottom up for fast, low-latency analytics. | Apache Impala is a distributed SQL query engine for Apache Hadoop that is modern and open source. |
| Developer | Designed by Facebook Community | Developed by Cloudera |
| Operation | Suitable for data-intensive Aggregation’s manipulations. | Suitable for complex Aggregation’s operations. |
| Storage | It stores Petabytes of data to run fast queries and uses MPP architecture to run interactive queries. | It uses medium size datasets and Data is stored in a columnar format, resulting in a high compression ratio and quick scanning. |
| Multi-table Queries | The performance analysis of presto is the same as Impala. | But Impala doesn’t support delete and update operations. And single table operations are performed well than Presto. |
| Components | Components include Manager nodes and workers. | It has three components like Planner, Coordinator, executor. |
| Response Time | Presto has a much faster response time and can swiftly resolve queries thanks to an expensive commercial solution. | Impala has a good response time compared to Presto where impala responses to one query in 15 sec. |
| Uses | Used by large-scale organizations like Facebook, Netflix, and Atlassian. data Scientists and analysts make use of Presto to execute a query. | Amazon Web Services and MapR give their hands to Impala. Impala is used by hammer, Stripe |
| Deploying in Cloud | Forms ideal workload in the cloud which provides availability and performance. Presto cluster is created whenever needed within a minute which helps in setup and cluster tuning. | As a daemon process, Impala avoids start-up overhead. It reads hive’s metadata and odbc driver. |
| Advantage | 1.Has no fault tolerance and works well with Amazon S3 Queries
2.Parallel computing process and careful handling of memory and data structure. 3. Executes Probabilistic Queries and provides approximate queries faster. |
1. It is easy for a data analyst and RDBMS to use because it uses HiveQL and SQL-92.
2. faster than other SQL Engines. |
| Disadvantage | 1. Insert and write queries on HDFS are not supported because it lacks its storage layer. | 1.low-latency interactive SQL query functions for HDFS and Hbase data.
2. The reliance on memory is substantial, and it is entirely reliant on the hive. 3. Custom binary files cannot be read directly; only text files can be read.
|
Presto installation is given here:
$ tar -zxf presto-server-0.149.tar.gz
$ cd presto-server-0.149
Presto Server Configuration
$ cd etc
$ vi config.properties
coordinator = true
node-scheduler.include-coordinator = true
http-server.http.port = 8080
query.max-memory = 5GB
query.max-memory-per-node = 1GB
discovery-server.enabled = true
discovery.uri = http://localhost:8080
Sample application in Presto
public class Prestodemo {
public static void main(String[] args) {
Connection connection = null;
Statement sta = null;
try {
Class.forName("com.facebook.presto.jdbc.PrestoDriver");
connection = DriverManager.getConnection(
"jdbc:presto://localhost:8080/mysql/demo", "test", “");
sta= connection.createStatement();
String sql;
sql = "select auth_id, auth_name from mysql.tutorials.author”;
}
}
Conclusion
Choosing the right database or SQL engine is entirely dependent on your needs. We’ve highlighted some of the most widely used and beneficial aspects of all SQL engines in this article. Through the specific features and properties, we listed in the comparison give easier choice for the user. We have the option of using Presto or Impala. The repository of choice is determined by technological specs and feature availability.
Recommended Articles
This is a guide to Presto vs Impala. Here we discuss the Presto vs Impala key differences with infographics and a comparison table. You may also have a look at the following articles to learn more –
