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Apache Kylin

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Apache Kylin vs Presto: What are the differences?

<Apache Kylin vs Presto>
  1. Architecture: Apache Kylin is an OLAP (Online Analytical Processing) engine that pre-builds and stores pre-aggregated data in a specialized format, while Presto is a distributed SQL query engine that runs ad-hoc queries on various data sources without pre-aggregation.

  2. Data Sources: Apache Kylin primarily works with Hadoop-based data sources like HDFS, Hive, and HBase, while Presto can query data from multiple sources such as HDFS, Apache Cassandra, MySQL, and more, providing greater flexibility in data connectivity.

  3. Use Cases: Apache Kylin is more suitable for scenarios that require complex OLAP operations and fast query performance on large datasets, making it a preferred choice for business intelligence and data warehousing applications. In contrast, Presto is ideal for running interactive queries on diverse data sources for real-time analytics and data exploration.

  4. Query Optimization: Apache Kylin optimizes query performance by storing pre-aggregated data cubes and utilizing a new generation of MOLAP (Multidimensional OLAP) technology, resulting in significantly faster query response times. On the other hand, Presto focuses on query parallelism and efficient data locality to optimize query processing speed.

  5. Community Support: Apache Kylin has a dedicated open-source community that actively maintains and updates the project, providing continuous support and enhancements. Meanwhile, Presto also has a strong community backing but is more widely adopted by tech companies like Facebook and Airbnb for large-scale data processing.

  6. Scaling Capabilities: Apache Kylin's performance may degrade with exponentially increasing data volumes due to the limitations of pre-aggregated data storage, whereas Presto can scale horizontally to handle massive amounts of data by adding more compute resources dynamically, making it highly resilient to growing workloads.

In Summary, Apache Kylin and Presto differ in their underlying architecture, supported data sources, use cases, query optimization techniques, community support, and scaling capabilities.

Decisions about Apache Kylin and Presto
Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 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 · 211K 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 Apache Kylin
Pros of Presto
  • 7
    Star schema and snowflake schema support
  • 5
    Seamless BI integration
  • 4
    OLAP on Hadoop
  • 3
    Easy install
  • 3
    Sub-second latency on extreme large dataset
  • 2
    ANSI-SQL
  • 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 Apache Kylin?

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

What is Presto?

Distributed SQL Query Engine for Big Data

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What companies use Apache Kylin?
What companies use Presto?
See which teams inside your own company are using Apache Kylin or Presto.
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What tools integrate with Apache Kylin?
What tools integrate with Presto?

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What are some alternatives to Apache Kylin 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.
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.
Apache Impala
Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.
AtScale
Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
Clickhouse
It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query.
See all alternatives