blob: 15f5d76e4bf35fe37452a0fcb7c8c58ccb8047d3 [file] [log] [blame]
// 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
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
= Introducing Apache Kudu
:author: Kudu Team
:imagesdir: ./images
:icons: font
:toc: left
:toclevels: 3
:doctype: book
:backend: html5
Kudu is a distributed columnar storage engine optimized for OLAP workloads.
Kudu runs on commodity hardware, is horizontally scalable, and supports highly
available operation.
Kudu's design sets it apart. Some of Kudu's benefits include:
- Fast processing of OLAP workloads.
- Strong but flexible consistency model, allowing you to choose consistency
requirements on a per-request basis, including the option for
strict-serializable consistency.
- Structured data model.
- Strong performance for running sequential and random workloads simultaneously.
- Tight integration with Apache Impala, making it a good, mutable alternative to
using HDFS with Apache Parquet.
- Integration with Apache NiFi and Apache Spark.
- Integration with Hive Metastore (HMS) and Apache Ranger to provide
fine-grain authorization and access control.
- Authenticated and encrypted RPC communication.
- High availability: Tablet Servers and Masters use the <<raft>>, which ensures
that as long as more than half the total number of tablet replicas is
available, the tablet is available for reads and writes. For instance,
if 2 out of 3 replicas (or 3 out of 5 replicas, etc.) are available,
the tablet is available. Reads can be serviced by read-only follower tablet
replicas, even in the event of a leader replica's failure.
- Automatic fault detection and self-healing: to keep data highly available,
the system detects failed tablet replicas and re-replicates data from
available ones, so failed replicas are automatically replaced when enough
Tablet Servers are available in the cluster.
- Location awareness (a.k.a. rack awareness) to keep the system available
in case of correlated failures and allowing Kudu clusters to span over
multiple availability zones.
- Logical backup (full and incremental) and restore.
- Multi-row transactions (only for INSERT/INSERT_IGNORE operations as of
Kudu 1.15 release).
- Easy to administer and manage.
By combining all of these properties, Kudu targets support for families of
applications that are difficult or impossible to implement using Hadoop storage
technologies, while it is compatible with most of the data processing
frameworks in the Hadoop ecosystem.
A few examples of applications for which Kudu is a great solution are:
* Reporting applications where newly-arrived data needs to be immediately available for end users
* Time-series applications that must simultaneously support:
- queries across large amounts of historic data
- granular queries about an individual entity that must return very quickly
* Applications that use predictive models to make real-time decisions with periodic
refreshes of the predictive model based on all historic data
For more information about these and other scenarios, see <<kudu_use_cases>>.
== Kudu-Impala Integration Features
Impala supports creating, altering, and dropping tables using Kudu as the persistence layer.
The tables follow the same internal / external approach as other tables in Impala,
allowing for flexible data ingestion and querying.
Data can be inserted into Kudu tables in Impala using the same syntax as
any other Impala table like those using HDFS or HBase for persistence.
Impala supports the `UPDATE` and `DELETE` SQL commands to modify existing data in
a Kudu table row-by-row or as a batch. The syntax of the SQL commands is chosen
to be as compatible as possible with existing standards. In addition to simple `DELETE`
or `UPDATE` commands, you can specify complex joins with a `FROM` clause in a subquery.
Flexible Partitioning::
Similar to partitioning of tables in Hive, Kudu allows you to dynamically
pre-split tables by hash or range into a predefined number of tablets, in order
to distribute writes and queries evenly across your cluster. You can partition by
any number of primary key columns, by any number of hashes, and an optional list of
split rows. See <<schema_design.adoc#schema_design,Schema Design>>.
Parallel Scan::
To achieve the highest possible performance on modern hardware, the Kudu client
used by Impala parallelizes scans across multiple tablets.
High-efficiency queries::
Where possible, Impala pushes down predicate evaluation to Kudu, so that predicates
are evaluated as close as possible to the data. Query performance is comparable
to Parquet in many workloads.
For more details regarding querying data stored in Kudu using Impala, please
refer to the Impala documentation.
== Concepts and Terms
.Columnar Data Store
Kudu is a _columnar data store_. A columnar data store stores data in strongly-typed
columns. With a proper design, it is superior for analytical or data warehousing
workloads for several reasons.
Read Efficiency:: For analytical queries, you can read a single column, or a portion
of that column, while ignoring other columns. This means you can fulfill your query
while reading a minimal number of blocks on disk. With a row-based store, you need
to read the entire row, even if you only return values from a few columns.
Data Compression:: Because a given column contains only one type of data,
pattern-based compression can be orders of magnitude more efficient than
compressing mixed data types, which are used in row-based solutions. Combined
with the efficiencies of reading data from columns, compression allows you to
fulfill your query while reading even fewer blocks from disk. See
<<schema_design.adoc#encoding,Data Compression>>
A _table_ is where your data is stored in Kudu. A table has a schema and
a totally ordered primary key. A table is split into segments called tablets.
A _tablet_ is a contiguous segment of a table, similar to a _partition_ in
other data storage engines or relational databases. A given tablet is
replicated on multiple tablet servers, and at any given point in time,
one of these replicas is considered the leader tablet. Any replica can service
reads, and writes require consensus among the set of tablet servers serving the tablet.
.Tablet Server
A _tablet server_ stores and serves tablets to clients. For a
given tablet, one tablet server acts as a leader, and the others act as
follower replicas of that tablet. Only leaders service write requests, while
leaders or followers each service read requests. Leaders are elected using
<<raft>>. One tablet server can serve multiple tablets, and one tablet can be served
by multiple tablet servers.
The _master_ keeps track of all the tablets, tablet servers, the
<<catalog_table>>, and other metadata related to the cluster. At a given point
in time, there can only be one acting master (the leader). If the current leader
disappears, a new master is elected using <<raft>>.
The master also coordinates metadata operations for clients. For example, when
creating a new table, the client internally sends the request to the master. The
master writes the metadata for the new table into the catalog table, and
coordinates the process of creating tablets on the tablet servers.
All the master's data is stored in a tablet, which can be replicated to all the
other candidate masters.
Tablet servers heartbeat to the master at a set interval (the default is once
per second).
.Raft Consensus Algorithm
Kudu uses the link:[Raft consensus algorithm] as
a means to guarantee fault-tolerance and consistency, both for regular tablets and for master
data. Through Raft, multiple replicas of a tablet elect a _leader_, which is responsible
for accepting and replicating writes to _follower_ replicas. Once a write is persisted
in a majority of replicas it is acknowledged to the client. A given group of `N` replicas
(usually 3 or 5) is able to accept writes with at most `(N - 1)/2` faulty replicas.
.Catalog Table
The _catalog table_ is the central location for
metadata of Kudu. It stores information about tables and tablets. The catalog
table may not be read or written directly. Instead, it is accessible
only via metadata operations exposed in the client API.
The catalog table stores two categories of metadata:
Tables:: table schemas, locations, and states
Tablets:: the list of existing tablets, which tablet servers have replicas of
each tablet, the tablet's current state, and start and end keys.
.Logical Replication
Kudu replicates operations, not on-disk data. This is referred to as _logical replication_,
as opposed to _physical replication_. This has several advantages:
* Although inserts and updates do transmit data over the network, deletes do not need
to move any data. The delete operation is sent to each tablet server, which performs
the delete locally.
* Physical operations, such as compaction, do not need to transmit the data over the
network in Kudu. This is different from storage systems that use HDFS, where
the blocks need to be transmitted over the network to fulfill the required number of
* Tablets do not need to perform compactions at the same time or on the same schedule,
or otherwise remain in sync on the physical storage layer. This decreases the chances
of all tablet servers experiencing high latency at the same time, due to compactions
or heavy write loads.
== Architectural Overview
The following diagram shows a Kudu cluster with three masters and multiple tablet
servers, each serving multiple tablets. It illustrates how Raft consensus is used
to allow for both leaders and followers for both the masters and tablet servers. In
addition, a tablet server can be a leader for some tablets, and a follower for others.
Leaders are shown in gold, while followers are shown in blue.
image::kudu-architecture-2.png[Kudu Architecture, 800]
== Example Use Cases
.Streaming Input with Near Real Time Availability
A common challenge in data analysis is one where new data arrives rapidly and constantly,
and the same data needs to be available in near real time for reads, scans, and
updates. Kudu offers the powerful combination of fast inserts and updates with
efficient columnar scans to enable real-time analytics use cases on a single storage layer.
.Time-series application with widely varying access patterns
A time-series schema is one in which data points are organized and keyed according
to the time at which they occurred. This can be useful for investigating the
performance of metrics over time or attempting to predict future behavior based
on past data. For instance, time-series customer data might be used both to store
purchase click-stream history and to predict future purchases, or for use by a
customer support representative. While these different types of analysis are occurring,
inserts and mutations may also be occurring individually and in bulk, and become available
immediately to read workloads. Kudu can handle all of these access patterns
simultaneously in a scalable and efficient manner.
Kudu is a good fit for time-series workloads for several reasons. With Kudu's support for
hash-based partitioning, combined with its native support for compound row keys, it is
simple to set up a table spread across many servers without the risk of "hotspotting"
that is commonly observed when range partitioning is used. Kudu's columnar storage engine
is also beneficial in this context, because many time-series workloads read only a few columns,
as opposed to the whole row.
In the past, you might have needed to use multiple data stores to handle different
data access patterns. This practice adds complexity to your application and operations,
and duplicates your data, doubling (or worse) the amount of storage
required. Kudu can handle all of these access patterns natively and efficiently,
without the need to off-load work to other data stores.
.Predictive Modeling
Data scientists often develop predictive learning models from large sets of data. The
model and the data may need to be updated or modified often as the learning takes
place or as the situation being modeled changes. In addition, the scientist may want
to change one or more factors in the model to see what happens over time. Updating
a large set of data stored in files in HDFS is resource-intensive, as each file needs
to be completely rewritten. In Kudu, updates happen in near real time. The scientist
can tweak the value, re-run the query, and refresh the graph in seconds or minutes,
rather than hours or days. In addition, batch or incremental algorithms can be run
across the data at any time, with near-real-time results.
.Combining Data In Kudu With Legacy Systems
Companies generate data from multiple sources and store it in a variety of systems
and formats. For instance, some of your data may be stored in Kudu, some in a traditional
RDBMS, and some in files in HDFS. You can access and query all of these sources and
formats using Impala, without the need to change your legacy systems.
== Next Steps
- link:quickstart.html[Get Started With Kudu]
- link:installation.html[Installing Kudu]