mirror of https://github.com/apache/druid.git
83 lines
4.2 KiB
Markdown
83 lines
4.2 KiB
Markdown
---
|
|
id: druid-vs-sql-on-hadoop
|
|
title: "Apache Druid vs SQL-on-Hadoop"
|
|
---
|
|
|
|
<!--
|
|
~ Licensed to the Apache Software Foundation (ASF) under one
|
|
~ or more contributor license agreements. See the NOTICE file
|
|
~ distributed with this work for additional information
|
|
~ regarding copyright ownership. The ASF licenses this file
|
|
~ to you under the Apache License, Version 2.0 (the
|
|
~ "License"); you may not use this file except in compliance
|
|
~ with the License. You may obtain a copy of the License at
|
|
~
|
|
~ http://www.apache.org/licenses/LICENSE-2.0
|
|
~
|
|
~ Unless required by applicable law or agreed to in writing,
|
|
~ software distributed under the License is distributed on an
|
|
~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
|
~ KIND, either express or implied. See the License for the
|
|
~ specific language governing permissions and limitations
|
|
~ under the License.
|
|
-->
|
|
|
|
|
|
SQL-on-Hadoop engines provide an
|
|
execution engine for various data formats and data stores, and
|
|
many can be made to push down computations down to Druid, while providing a SQL interface to Druid.
|
|
|
|
For a direct comparison between the technologies and when to only use one or the other, things basically comes down to your
|
|
product requirements and what the systems were designed to do.
|
|
|
|
Druid was designed to
|
|
|
|
1. be an always on service
|
|
1. ingest data in real-time
|
|
1. handle slice-n-dice style ad-hoc queries
|
|
|
|
SQL-on-Hadoop engines generally sidestep Map/Reduce, instead querying data directly from HDFS or, in some cases, other storage systems.
|
|
Some of these engines (including Impala and Presto) can be colocated with HDFS data nodes and coordinate with them to achieve data locality for queries.
|
|
What does this mean? We can talk about it in terms of three general areas
|
|
|
|
1. Queries
|
|
1. Data Ingestion
|
|
1. Query Flexibility
|
|
|
|
### Queries
|
|
|
|
Druid segments stores data in a custom column format. Segments are scanned directly as part of queries and each Druid server
|
|
calculates a set of results that are eventually merged at the Broker level. This means the data that is transferred between servers
|
|
are queries and results, and all computation is done internally as part of the Druid servers.
|
|
|
|
Most SQL-on-Hadoop engines are responsible for query planning and execution for underlying storage layers and storage formats.
|
|
They are processes that stay on even if there is no query running (eliminating the JVM startup costs from Hadoop MapReduce).
|
|
Some (Impala/Presto) SQL-on-Hadoop engines have daemon processes that can be run where the data is stored, virtually eliminating network transfer costs. There is still
|
|
some latency overhead (e.g. serde time) associated with pulling data from the underlying storage layer into the computation layer. We are unaware of exactly
|
|
how much of a performance impact this makes.
|
|
|
|
### Data Ingestion
|
|
|
|
Druid is built to allow for real-time ingestion of data. You can ingest data and query it immediately upon ingestion,
|
|
the latency between how quickly the event is reflected in the data is dominated by how long it takes to deliver the event to Druid.
|
|
|
|
SQL-on-Hadoop, being based on data in HDFS or some other backing store, are limited in their data ingestion rates by the
|
|
rate at which that backing store can make data available. Generally, the backing store is the biggest bottleneck for
|
|
how quickly data can become available.
|
|
|
|
### Query Flexibility
|
|
|
|
Druid's query language is fairly low level and maps to how Druid operates internally. Although Druid can be combined with a high level query
|
|
planner such as [Plywood](https://github.com/implydata/plywood) to support most SQL queries and analytic SQL queries (minus joins among large tables),
|
|
base Druid is less flexible than SQL-on-Hadoop solutions for generic processing.
|
|
|
|
SQL-on-Hadoop support SQL style queries with full joins.
|
|
|
|
## Druid vs Parquet
|
|
|
|
Parquet is a column storage format that is designed to work with SQL-on-Hadoop engines. Parquet doesn't have a query execution engine, and instead
|
|
relies on external sources to pull data out of it.
|
|
|
|
Druid's storage format is highly optimized for linear scans. Although Druid has support for nested data, Parquet's storage format is much
|
|
more hierachical, and is more designed for binary chunking. In theory, this should lead to faster scans in Druid.
|