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Hibernate vs Presto: What are the differences?
#### Introduction
In this section, we will highlight the key differences between Hibernate and Presto.
1. **Data Processing Capability**: Hibernate is an Object-Relational Mapping (ORM) tool that allows mapping Java classes to database tables, whereas Presto focuses on distributed SQL query processing over a variety of data sources.
2. **Use Case**: Hibernate is commonly used as a persistence layer in Java applications to facilitate the interaction between Java objects and a relational database, while Presto is preferred for analytics and business intelligence tasks where querying large datasets with high performance is crucial.
3. **Scalability**: Hibernate is typically used for single-node applications or small to medium-scale systems, whereas Presto is designed for distributed computing, offering the ability to scale up to handle massive amounts of data across multiple nodes.
4. **Query Language Support**: Hibernate supports HQL (Hibernate Query Language) which is similar to SQL but operates on objects, while Presto supports ANSI SQL allowing users to perform complex queries across different data sources seamlessly.
5. **Ecosystem Integration**: Hibernate is often integrated with frameworks like Spring for building enterprise applications, while Presto seamlessly integrates with various ecosystem tools such as Hive, Hadoop, and Kafka for data processing and analytics workflows.
6. **Performance Optimization**: Hibernate focuses on providing a simplified way to interact with databases using objects, but may face performance issues with large datasets, whereas Presto is optimized for handling complex analytical queries efficiently and can achieve high-performance results when dealing with big data.
In Summary, the key differences between Hibernate and Presto lie in their focus on data processing capability, use case scenarios, scalability, query language support, ecosystem integration, and performance optimization for different types of applications and workloads.
To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.
Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.
We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.
Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.
Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.
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The platform deals with time series data from sensors aggregated against things( event data that originates at periodic intervals). We use Cassandra as our distributed database to store time series data. Aggregated data insights from Cassandra is delivered as web API for consumption from other applications. Presto as a distributed sql querying engine, can provide a faster execution time provided the queries are tuned for proper distribution across the cluster. Another objective that we had was to combine Cassandra table data with other business data from RDBMS or other big data systems where presto through its connector architecture would have opened up a whole lot of options for us.
Pros of Hibernate
- Easy ORM22
- Easy transaction definition8
- Is integrated with spring jpa3
- Open Source1
Pros of Presto
- Works directly on files in s3 (no ETL)18
- Open-source13
- Join multiple databases12
- Scalable10
- Gets ready in minutes7
- MPP6
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Cons of Hibernate
- Can't control proxy associations when entity graph used3
Cons of Presto
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