cockroach

README

A Scalable, Geo-Replicated, Transactional Datastore

Table of Contents

• What is CockroachDB
• Status
• Running CockroachDB Locally
• Deploying CockroachDB in production
• Getting in touch and contributing
• Talks
• Design and Datastore Goal Articulation
• Architecture and Client Architecture

What is CockroachDB

CockroachDB is a distributed SQL database built on top of a transactional and consistent key:value store. The primary design goals are support for ACID transactions, horizontal scalability, and survivability, hence the name. CockroachDB implements a Raft consensus algorithm for consistency. It aims to tolerate disk, machine, rack, and even datacenter failures with minimal latency disruption and no manual intervention. CockroachDB nodes (RoachNodes) are symmetric; a design goal is homogenous deployment (one binary) with minimal configuration.

Status

CockroachDB is currently in alpha. See our Roadmap and Issues for a list of features planned or in development.

Running CockroachDB Locally

Environment Setup

Native (read: without Docker)

• set up the dev environment (see CONTRIBUTING.md)
• make build

Using Docker

Install Docker! On OSX (official docs):

# install docker and docker-machine:
$ brew install docker docker-machine
# install VirtualBox:
$ brew cask install virtualbox
# create the VM (this will also start it):
$ docker-machine create --driver virtualbox default
# if the VM exists but isn't running, start it:
$ docker-machine start default
# set up the environment for the docker client:
$ eval $(docker-machine env default)

Other operating systems will have a similar set of commands. Please check Docker's documentation for more info.

Pull the CockroachDB Docker image and drop into a shell within it:

docker pull cockroachdb/cockroach
docker run -t -i cockroachdb/cockroach shell
# root@82cb657cdc42:/cockroach#

Bootstrap and talk to a single node

Note: If you’re using Docker as described above, run all the commands described below in the container’s shell.

Setting up Cockroach is easy, but starting a test node is even easier. All it takes is running:

./cockroach start --dev &

Built-in client

Now let's talk to this node. The easiest way to do that is to use the cockroach binary - it comes with a built-in sql client:

./cockroach sql --dev
# Welcome to the cockroach SQL interface.
# All statements must be terminated by a semicolon.
# To exit: CTRL + D.
192.168.99.100:26257> show databases;
+----------+
| Database |
+----------+
| "system" |
+----------+
192.168.99.100:26257> SET database = system;
OK
192.168.99.100:26257> show tables;
+--------------+
|    Table     |
+--------------+
| "descriptor" |
| "namespace"  |
| "users"      |
| "zones"      |
+--------------+

Check out ./cockroach help to see all available commands.

Deploying CockroachDB in production

To run a CockroachDB cluster on various cloud platforms using docker machine, see cockroach-prod.

Running a multi-node cluster

We'll set up a three-node cluster below.

The code assumes that $NODE{1,2,3} are the host names of the three nodes in the cluster.

# Create certificates
./cockroach cert create-ca
./cockroach cert create-node 127.0.0.1 localhost $NODE1 $NODE2 $NODE3
./cockroach cert create-client root
# Distribute certificates
for n in $NODE1 $NODE2 $NODE3; do
  scp -r certs ${n}:certs
done

Now, on node 1, initialize the cluster (to /data; yours may vary):

./cockroach init --stores=ssd=/data

Then, start the cluster by starting each individual node.

./cockroach start --stores=ssd=/data --gossip=${NODE1}:26257,${NODE2}:26257,${NODE3}:26257

Verify that the cluster is connected on the web UI by directing your browser at

https://<any_node>:26257

Get in touch

We spend almost all of our time here on GitHub, and use the issue tracker for bug reports.

For development related questions and anything else, message our mailing list at cockroach-db@googlegroups.com. We recommend joining before posting, or your messages may be held back for moderation.

Contributing

We're an Open Source project and welcome contributions. See CONTRIBUTING.md to get your local environment set up. Once that's done, take a look at our open issues, in particular those with the helpwanted label, and follow our code reviews to learn about our style and conventions.

Talks

• Venue: Golang UK Conference 2015, by Ben Darnell on (09/10/2015), 52min.
  
• Venue: Data Driven NYC, by [Spencer Kimball] (https://github.com/spencerkimball) on (06/16/2015), 23min.
  A short, less technical presentation of CockroachDB.
• Venue: NY Enterprise Technology Meetup, by Tobias Schottdorf on (06/10/2015), 15min.
  A short, non-technical talk with a small cluster survivability demo.
• Venue: CoreOS Fest, by Spencer Kimball on (05/27/2015), 25min.
  An introduction to the goals and design of CockroachDB. The recommended talk to watch if all you have time for is one.
• Venue: The Go Devroom FOSDEM 2015, by Tobias Schottdorf on (03/04/2015), 45min.
  The most technical talk given thus far, going through the implementation of transactions in some detail.

Older talks

• Venue: The NoSQL User Group Cologne, by Tobias Schottdorf on (11/5/2014), 1h25min.
• Venue: Yelp!, by Spencer Kimball on (9/5/2014), 1h.

Design

This is an overview. For an in depth discussion of the design, see the design doc.

For a quick design overview, see the CockroachDB tech talk slides or watch a presentation.

CockroachDB is a distributed SQL database built on top of a transactional and consistent key:value store. The primary design goals are support for ACID transactions, horizontal scalability and survivability, hence the name. CockroachDB implements a Raft consensus algorithm for consistency. It aims to tolerate disk, machine, rack, and even datacenter failures with minimal latency disruption and no manual intervention. CockroachDB nodes (RoachNodes) are symmetric; a design goal is homogenous deployment (one binary) with minimal configuration.

CockroachDB implements a single, monolithic sorted map from key to value where both keys and values are byte strings (not unicode). CockroachDB scales linearly (theoretically up to 4 exabytes (4E) of logical data). The map is composed of one or more ranges and each range is backed by data stored in RocksDB (a variant of LevelDB), and is replicated to a total of three or more CockroachDB servers. Ranges are defined by start and end keys. Ranges are merged and split to maintain total byte size within a globally configurable min/max size interval. Range sizes default to target 64M in order to facilitate quick splits and merges and to distribute load at hotspots within a key range. Range replicas are intended to be located in disparate datacenters for survivability (e.g. { US-East, US-West, Japan }, { Ireland, US-East, US-West}, { Ireland, US-East, US-West, Japan, Australia }).

Single mutations to ranges are mediated via an instance of a distributed consensus algorithm to ensure consistency. We’ve chosen to use the Raft consensus algorithm. All consensus state is stored in RocksDB.

A single logical mutation may affect multiple key/value pairs. Logical mutations have ACID transactional semantics. If all keys affected by a logical mutation fall within the same range, atomicity and consistency are guaranteed by Raft; this is the fast commit path. Otherwise, a non-locking distributed commit protocol is employed between affected ranges.

CockroachDB provides snapshot isolation (SI) and serializable snapshot isolation (SSI) semantics, allowing externally consistent, lock-free reads and writes--both from an historical snapshot timestamp and from the current wall clock time. SI provides lock-free reads and writes but still allows write skew. SSI eliminates write skew, but introduces a performance hit in the case of a contentious system. SSI is the default isolation; clients must consciously decide to trade correctness for performance. CockroachDB implements a limited form of linearalizability, providing ordering for any observer or chain of observers.

Similar to Spanner directories, CockroachDB allows configuration of arbitrary zones of data. This allows replication factor, storage device type, and/or datacenter location to be chosen to optimize performance and/or availability. Unlike Spanner, zones are monolithic and don’t allow movement of fine grained data on the level of entity groups.

A Megastore-like message queue mechanism is also provided to 1) efficiently sideline updates which can tolerate asynchronous execution and 2) provide an integrated message queuing system for asynchronous communication between distributed system components.

SQL - NoSQL - NewSQL Capabilities

Datastore Goal Articulation

There are other important axes involved in data-stores which are less well understood and/or explained. There is lots of cross-dependency, but it's safe to segregate two more of them as (a) scan efficiency, and (b) read vs write optimization.

Datastore Scan Efficiency Spectrum

Scan efficiency refers to the number of IO ops required to scan a set of sorted adjacent rows matching a criteria. However, it's a complicated topic, because of the options (or lack of options) for controlling physical order in different systems.

• Some designs either default to or only support "heap organized" physical records (Oracle, MySQL, Postgres, SQLite, MongoDB). In this design, a naive sorted-scan of an index involves one IO op per record.
• In these systems it's possible to "fully cover" a sorted-query in an index with some write-amplification.
• In some systems it's possible to put the primary record data in a sorted btree instead of a heap-table (default in MySQL/Innodb, option in Oracle).
• Sorted-order LSM NoSQL could be considered index-organized-tables, with efficient scans by the row-key. (HBase).
• Some NoSQL is not optimized for sorted-order retrieval, because of hash-bucketing, primarily based on the Dynamo design. (Cassandra, Riak)

Read vs. Write Optimization Spectrum

Read vs write optimization is a product of the underlying sorted-order data-structure used. Btrees are read-optimized. Hybrid write-deferred trees are a balance of read-and-write optimizations (shuttle-trees, fractal-trees, stratified-trees). LSM separates write-incorporation into a separate step, offering a tunable amount of read-to-write optimization. An "ideal" LSM at 0%-write-incorporation is a log, and at 100%-write-incorporation is a btree.

The topic of LSM is confused by the fact that LSM is not an algorithm, but a design pattern, and usage of LSM is hindered by the lack of a de-facto optimal LSM design. LevelDB/RocksDB is one of the more practical LSM implementations, but it is far from optimal. Popular text-indicies like Lucene are non-general purpose instances of write-optimized LSM.

Further, there is a dependency between access pattern (read-modify-write vs blind-write and write-fraction), cache-hitrate, and ideal sorted-order algorithm selection. At a certain write-fraction and read-cache-hitrate, systems achieve higher total throughput with write-optimized designs, at the cost of increased worst-case read latency. As either write-fraction or read-cache-hitrate approaches 1.0, write-optimized designs provide dramatically better sustained system throughput when record-sizes are small relative to IO sizes.

Given this information, data-stores can be sliced by their sorted-order storage algorithm selection. Btree stores are read-optimized (Oracle, SQLServer, Postgres, SQLite2, MySQL, MongoDB, CouchDB), hybrid stores are read-optimized with better write-throughput (Tokutek MySQL/MongoDB), while LSM-variants are write-optimized (HBase, Cassandra, SQLite3/LSM, CockroachDB).

Architecture

CockroachDB implements a layered architecture, with various subdirectories implementing layers as appropriate. The highest level of abstraction is the SQL layer, which depends directly on the structured data API. The structured data API provides familiar relational concepts such as schemas, tables, columns, and indexes. The structured data API in turn depends on the distributed key value store (kv/). The distributed key value store handles the details of range addressing to provide the abstraction of a single, monolithic key value store. It communicates with any number of RoachNodes (server/), storing the actual data. Each node contains one or more stores (storage/), one per physical device.

Each store contains potentially many ranges, the lowest-level unit of key-value data. Ranges are replicated using the Raft consensus protocol. The diagram below is a blown up version of stores from four of the five nodes in the previous diagram. Each range is replicated three ways using raft. The color coding shows associated range replicas.

Client Architecture

RoachNodes serve client traffic using a fully-featured SQL API which accepts requests as either application/x-protobuf or application/json. Client implementations consist of an HTTP sender (transport) and a transactional sender which implements a simple exponential backoff / retry protocol, depending on CockroachDB error codes.

The DB client gateway accepts incoming requests and sends them through a transaction coordinator, which handles transaction heartbeats on behalf of clients, provides optimization pathways, and resolves write intents on transaction commit or abort. The transaction coordinator passes requests onto a distributed sender, which looks up index metadata, caches the results, and routes internode RPC traffic based on where the index metadata indicates keys are located in the distributed cluster.

In addition to the gateway for external DB client traffic, each RoachNode provides the full key/value API (including all internal methods) via a Go RPC server endpoint. The RPC server endpoint forwards requests to one or more local stores depending on the specified key range.

Internally, each RoachNode uses the Go implementation of the CockroachDB client in order to transactionally update system key/value data; for example during split and merge operations to update index metadata records. Unlike an external application, the internal client eschews the HTTP sender and instead directly shares the transaction coordinator and distributed sender used by the DB client gateway.