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Hue vs Presto: What are the differences?

Introduction

Hue and Presto are both powerful tools used in the field of data analytics and processing. Although they have some similarities in terms of functionality, there are several key differences that set them apart from each other.

  1. Query Interfaces: Hue provides a user-friendly web-based interface for searching and querying data. It allows users to write queries in SQL, Impala, Hive, and other query languages without having to write complex code. On the other hand, Presto offers a command-line interface (CLI) for executing queries. Users need to have a good understanding of SQL and command-line operations to effectively use Presto.

  2. Processing Architecture: Hue is primarily designed to work with Apache Hadoop and its related technologies, such as Hive and Impala. It leverages the processing power of these technologies to analyze and manipulate large volumes of data. On the contrary, Presto is a distributed SQL query engine that is designed to query data from a variety of sources beyond Hadoop, including relational databases, cloud storage, and NoSQL databases.

  3. Development and Integration: Hue provides a rich set of development tools and integrations, making it easier for developers to build applications on top of it. It supports the integration of external libraries and frameworks, such as Spark and TensorFlow, allowing users to extend its functionalities. In contrast, Presto focuses more on executing SQL queries efficiently and does not offer the same level of development and integration capabilities as Hue.

  4. Performance and Scalability: Hue is known for its user-friendly interface and ease of use, but it may not be the best option when it comes to performance and scalability. As it relies on other underlying technologies, the performance of Hue can be affected by the capabilities and limitations of those technologies. On the other hand, Presto is specifically designed for high-performance and scalable query processing. It uses a distributed architecture and in-memory computing to deliver faster query results, especially for complex and ad-hoc queries.

  5. Security and Authentication: Hue provides a comprehensive security framework that allows users to secure their data and control access to it. It supports authentication mechanisms like LDAP and Active Directory, enabling fine-grained access control. Presto also provides security features, but they may not be as extensive as those offered by Hue. It primarily relies on the security features provided by the underlying data sources and does not have built-in authentication mechanisms.

  6. Data Source Compatibility: Hue has excellent compatibility with data sources that are part of the Apache Hadoop ecosystem, including HDFS, Hive, and Impala. It provides a unified interface to query and analyze data from these sources. On the other hand, Presto is designed to be compatible with a wide range of data sources, including traditional databases like MySQL and PostgreSQL, cloud object storage like Amazon S3, and NoSQL databases like Cassandra and MongoDB.

In Summary, Hue and Presto differ in several key aspects, including their query interfaces, processing architectures, development and integration capabilities, performance and scalability, security and authentication features, and data source compatibility.

Decisions about Hue and Presto
Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 2.8M 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 · 207.9K 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 Hue
Pros of Presto
    Be the first to leave a pro
    • 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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    What is Hue?

    It is open source and lets regular users import their big data, query it, search it, visualize it and build dashboards on top of it, all from their browser.

    What is Presto?

    Distributed SQL Query Engine for Big Data

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    What tools integrate with Hue?
    What tools integrate with Presto?
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      What are some alternatives to Hue and Presto?
      Apache Spark
      Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
      Splunk
      It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
      Apache Flink
      Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
      Amazon Athena
      Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
      Apache Hive
      Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
      See all alternatives