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Druid vs Presto: What are the differences?

Introduction:

In this article, we will compare and analyze the key differences between Druid and Presto, two widely used data processing technologies.

  1. Scalability and Workload Management: Druid is designed for scalable, real-time analytics on large datasets. It excels at serving low-latency queries by pre-calculating and indexing data in a specialized data store. On the other hand, Presto is a distributed SQL query engine that focuses on providing fast ad hoc queries on various data sources, but it does not provide built-in support for real-time analytics or indexing.

  2. Data Storage and Query Capabilities: Druid stores data in column-oriented, compressed formats, enabling fast aggregations and filtering. It offers a wide range of advanced analytic features such as roll-ups, approximations, and time-series analysis. Presto, on the other hand, can query various data sources directly using standard SQL syntax, allowing it to access a broader range of data formats and systems, including relational databases and files.

  3. Processing Paradigm: Druid is a data processing system specifically optimized for time series data and event-driven analytics. It is built on the concept of data ingestion, indexing, and query execution. Presto, on the other hand, is a general-purpose distributed SQL query engine that can process data across multiple distributed nodes in a parallel and scalable manner. It can handle a wide range of query workloads, from simple ad hoc queries to highly complex analytical queries.

  4. Data Latency: Druid focuses on providing low-latency queries for real-time analytics. It achieves this by leveraging pre-calculated aggregations and an indexed data store. This makes it ideal for interactive, real-time dashboards and applications. Presto, while it can handle queries in near real-time, may have slightly higher latencies compared to Druid, especially when dealing with large datasets or complex analytical queries.

  5. Ecosystem and Integration: Druid has a strong ecosystem with built-in support for Apache Hadoop, Apache Kafka, and other big data technologies. It integrates well with popular BI tools and frameworks such as Apache Superset and Tableau. Presto, on the other hand, offers extensive integration capabilities with various data sources and systems, including Hadoop, Cassandra, MySQL, and more. It supports JDBC and ODBC connectors, making it compatible with a wide range of BI and visualization tools.

  6. Security and Authentication: Druid provides robust security features, including encryption at rest and in transit, role-based access control, and auditing capabilities. It supports integration with external authentication providers like LDAP and Kerberos. Presto also offers security features such as SSL encryption and access control, but it lacks some advanced security functionalities found in Druid, such as data encryption at rest.

In summary, Druid is designed for scalable, real-time analytics with low-latency queries, specialized indexing, and advanced analytic features. Presto, on the other hand, is a distributed SQL query engine that excels at ad hoc querying across various data sources, providing extensive compatibility and integration capabilities.

Decisions about Druid and Presto
Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3.3M views

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.

#BigData #AWS #DataScience #DataEngineering

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Karthik Raveendran
CPO at Attinad Software · | 3 upvotes · 217.9K views

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.

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Pros of Druid
Pros of Presto
  • 15
    Real Time Aggregations
  • 6
    Batch and Real-Time Ingestion
  • 5
    OLAP
  • 3
    OLAP + OLTP
  • 2
    Combining stream and historical analytics
  • 1
    OLTP
  • 18
    Works directly on files in s3 (no ETL)
  • 13
    Open-source
  • 12
    Join multiple databases
  • 10
    Scalable
  • 7
    Gets ready in minutes
  • 6
    MPP

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Cons of Druid
Cons of Presto
  • 3
    Limited sql support
  • 2
    Joins are not supported well
  • 1
    Complexity
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    What is Druid?

    Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

    What is Presto?

    Distributed SQL Query Engine for Big Data

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    What companies use Druid?
    What companies use Presto?
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    What tools integrate with Druid?
    What tools integrate with Presto?

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    What are some alternatives to Druid and Presto?
    HBase
    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    Prometheus
    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    See all alternatives