Deploy CockroachDB On-Premises (Insecure)

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This tutorial shows you how to manually deploy an insecure multi-node CockroachDB cluster on multiple machines, using HAProxy load balancers to distribute client traffic.

Before you begin

Requirements

• You must have SSH access to each machine. This is necessary for distributing and starting CockroachDB binaries.

• Your network configuration must allow TCP communication on the following ports:

  • 26257 for intra-cluster and client-cluster communication
  • 8080 to expose your DB Console

• Carefully review the Production Checklist and recommended Topology Patterns.

• Do not run multiple node processes on the same VM or machine. This defeats CockroachDB's replication and causes the system to be a single point of failure. Instead, start each node on a separate VM or machine.

• To start a node with multiple disks or SSDs, you can use either of these approaches:

  • Configure the disks or SSDs as a single RAID volume, then pass the RAID volume to the --store flag when starting the cockroach process on the node.
  • Provide a separate --store flag for each disk when starting the cockroach process on the node. For more details about stores, see Start a Node.
  Warning:

  If you start a node with multiple --store flags, it is not possible to scale back down to only using a single store on the node. Instead, you must decommission the node and start a new node with the updated --store.

• When starting each node, use the --locality flag to describe the node's location, for example, --locality=region=west,zone=us-west-1. The key-value pairs should be ordered from most to least inclusive, and the keys and order of key-value pairs must be the same on all nodes.

• When deploying in a single availability zone:

  • To be able to tolerate the failure of any 1 node, use at least 3 nodes with the default 3-way replication factor. In this case, if 1 node fails, each range retains 2 of its 3 replicas, a majority.
  • To be able to tolerate 2 simultaneous node failures, use at least 5 nodes and increase the default replication factor for user data to 5. The replication factor for important internal data is 5 by default, so no adjustments are needed for internal data. In this case, if 2 nodes fail at the same time, each range retains 3 of its 5 replicas, a majority.

• When deploying across multiple availability zones:

  • To be able to tolerate the failure of 1 entire AZ in a region, use at least 3 AZs per region and set --locality on each node to spread data evenly across regions and AZs. In this case, if 1 AZ goes offline, the 2 remaining AZs retain a majority of replicas.
  • To ensure that ranges are split evenly across nodes, use the same number of nodes in each AZ. This is to avoid overloading any nodes with excessive resource consumption.

• When deploying across multiple regions:

  • To be able to tolerate the failure of 1 entire region, use at least 3 regions.

Recommendations

• Consider using a secure cluster instead. Using an insecure cluster comes with risks:

  • Your cluster is open to any client that can access any node's IP addresses.
  • Any user, even root, can log in without providing a password.
  • Any user, connecting as root, can read or write any data in your cluster.
  • There is no network encryption or authentication, and thus no confidentiality.

• Decide how you want to access your DB Console:

  Access Level	Description
  Partially open	Set a firewall rule to allow only specific IP addresses to communicate on port 8080.
  Completely open	Set a firewall rule to allow all IP addresses to communicate on port 8080.
  Completely closed	Set a firewall rule to disallow all communication on port 8080. In this case, a machine with SSH access to a node could use an SSH tunnel to access the DB Console.

Step 1. Synchronize clocks

CockroachDB requires moderate levels of clock synchronization to preserve data consistency. For this reason, when a node detects that its clock is out of sync with at least half of the other nodes in the cluster by 80% of the maximum offset allowed (500ms by default), it spontaneously shuts down. This avoids the risk of consistency anomalies, but it's best to prevent clocks from drifting too far in the first place by running clock synchronization software on each node.

ntpd should keep offsets in the single-digit milliseconds, so that software is featured here, but other methods of clock synchronization are suitable as well.

1. SSH to the first machine.

2. Disable timesyncd, which tends to be active by default on some Linux distributions:

   $ sudo timedatectl set-ntp no
   

   Verify that timesyncd is off:

   $ timedatectl
   

   Look for Network time on: no or NTP enabled: no in the output.

3. Install the ntp package:

   $ sudo apt-get install ntp
   

4. Stop the NTP daemon:

   $ sudo service ntp stop
   

5. Sync the machine's clock with Google's NTP service:

   $ sudo ntpd -b time.google.com
   

   To make this change permanent, in the /etc/ntp.conf file, remove or comment out any lines starting with server or pool and add the following lines:

   server time1.google.com iburst
   server time2.google.com iburst
   server time3.google.com iburst
   server time4.google.com iburst
   

   Restart the NTP daemon:

   $ sudo service ntp start
   

   Note:

   We recommend Google's NTP service because it handles "smearing" the leap second. If you use a different NTP service that doesn't smear the leap second, be sure to configure client-side smearing in the same way on each machine. See the Production Checklist for details.

6. Verify that the machine is using a Google NTP server:

   $ sudo ntpq -p
   

   The active NTP server will be marked with an asterisk.

7. Repeat these steps for each machine where a CockroachDB node will run.

Step 2. Start nodes

You can start the nodes manually or automate the process using systemd.

Step 3. Initialize the cluster

On your local machine, complete the node startup process and have them join together as a cluster:

1. Install CockroachDB on your local machine, if you haven't already.

2. Run the cockroach init command, with the --host flag set to the address of any node:

   $ cockroach init --insecure --host=<address of any node on --join list>
   

   Each node then prints helpful details to the standard output, such as the CockroachDB version, the URL for the DB Console, and the SQL URL for clients.

Step 4. Test the cluster

CockroachDB replicates and distributes data behind-the-scenes and uses a Gossip protocol to enable each node to locate data across the cluster. Once a cluster is live, any node can be used as a SQL gateway.

When using a load balancer, you should issue commands directly to the load balancer, which then routes traffic to the nodes.

Use the built-in SQL client locally as follows:

1. On your local machine, launch the built-in SQL client, with the --host flag set to the address of the load balancer:

   $ cockroach sql --insecure --host=<address of load balancer>
   

2. Create an insecurenodetest database:

   > CREATE DATABASE insecurenodetest;
   

3. View the cluster's databases, which will include insecurenodetest:

   > SHOW DATABASES;
   

   +--------------------+
   |      Database      |
   +--------------------+
   | crdb_internal      |
   | information_schema |
   | insecurenodetest   |
   | pg_catalog         |
   | system             |
   +--------------------+
   (5 rows)
   

4. Use \q to exit the SQL shell.

Step 5. Set up load balancing

Each CockroachDB node is an equally suitable SQL gateway to your cluster, but to ensure client performance and reliability, it's important to use load balancing:

• Performance: Load balancers spread client traffic across nodes. This prevents any one node from being overwhelmed by requests and improves overall cluster performance (queries per second).

• Reliability: Load balancers decouple client health from the health of a single CockroachDB node. In cases where a node fails, the load balancer redirects client traffic to available nodes.

  Tip:

  With a single load balancer, client connections are resilient to node failure, but the load balancer itself is a point of failure. It's therefore best to make load balancing resilient as well by using multiple load balancing instances, with a mechanism like floating IPs or DNS to select load balancers for clients.

HAProxy is one of the most popular open-source TCP load balancers, and CockroachDB includes a built-in command for generating a configuration file that is preset to work with your running cluster, so we feature that tool here.

1. SSH to the machine where you want to run HAProxy.

2. Install HAProxy:

  $ apt-get install haproxy

1. Download the CockroachDB archive for Linux, and extract the binary:

   $ curl https://binaries.cockroachdb.com/cockroach-v25.1.0-alpha.1.linux-amd64.tgz \
   | tar -xz
   

2. Copy the binary into the PATH:

   $ cp -i cockroach-v25.1.0-alpha.1.linux-amd64/cockroach /usr/local/bin/
   

If you get a permissions error, prefix the command with sudo.

1. Run the cockroach gen haproxy command, specifying the address of any CockroachDB node:

   $ cockroach gen haproxy --insecure \
   --host=<address of any node> \
   --port=26257
   

   By default, the generated configuration file is called haproxy.cfg and looks as follows, with the server addresses pre-populated correctly:

   global
     maxconn 4096
   
   defaults
       mode                tcp
       # Timeout values should be configured for your specific use.
       # See: https://cbonte.github.io/haproxy-dconv/1.8/configuration.html#4-timeout%20connect
       timeout connect     10s
       timeout client      1m
       timeout server      1m
       # TCP keep-alive on client side. Server already enables them.
       option              clitcpka
   
   listen psql
       bind :26257
       mode tcp
       balance roundrobin
       option httpchk GET /health?ready=1
       server cockroach1 <node1 address>:26257 check port 8080
       server cockroach2 <node2 address>:26257 check port 8080
       server cockroach3 <node3 address>:26257 check port 8080
   

   The file is preset with the minimal configurations needed to work with your running cluster:

   Field	Description
   timeout connect timeout client timeout server	Timeout values that should be suitable for most deployments.
   bind	The port that HAProxy listens on. This is the port clients will connect to and thus needs to be allowed by your network configuration. This tutorial assumes HAProxy is running on a separate machine from CockroachDB nodes. If you run HAProxy on the same machine as a node (not recommended), you'll need to change this port, as 26257 is likely already being used by the CockroachDB node.
   balance	The balancing algorithm. This is set to roundrobin to ensure that connections get rotated amongst nodes (connection 1 on node 1, connection 2 on node 2, etc.). Check the HAProxy Configuration Manual for details about this and other balancing algorithms.
   option httpchk	The HTTP endpoint that HAProxy uses to check node health. /health?ready=1 ensures that HAProxy doesn't direct traffic to nodes that are live but not ready to receive requests.
   server	For each included node, this field specifies the address the node advertises to other nodes in the cluster, i.e., the addressed pass in the --advertise-addr flag on node startup. Make sure hostnames are resolvable and IP addresses are routable from HAProxy.

   Note:

   For full details on these and other configuration settings, see the HAProxy Configuration Manual.

2. Start HAProxy, with the -f flag pointing to the haproxy.cfg file:

  $ haproxy -f haproxy.cfg

1. Repeat these steps for each additional instance of HAProxy you want to run.

Step 6. Run a sample workload

CockroachDB comes with a number of built-in workloads for simulating client traffic. This step features CockroachDB's version of the TPC-C workload.

1. SSH to the machine where you want the run the sample TPC-C workload.

   This should be a machine that is not running a CockroachDB node.

2. Download the CockroachDB archive for Linux, and extract the binary:

   $ curl https://binaries.cockroachdb.com/cockroach-v25.1.0-alpha.1.linux-amd64.tgz \
   | tar -xz
   

3. Copy the binary into the PATH:

   $ cp -i cockroach-v25.1.0-alpha.1.linux-amd64/cockroach /usr/local/bin/
   

   If you get a permissions error, prefix the command with sudo.

4. Use the cockroach workload command to load the initial schema and data, pointing it at the IP address of the load balancer:

   $ cockroach workload init tpcc \
   'postgresql://root@<IP ADDRESS OF LOAD BALANCER>:26257/tpcc?sslmode=disable'
   

5. Use the cockroach workload command to run the workload for 10 minutes:

   $ cockroach workload run tpcc \
   --duration=10m \
   'postgresql://root@<IP ADDRESS OF LOAD BALANCER>:26257/tpcc?sslmode=disable'
   

   You'll see per-operation statistics print to standard output every second:

   _elapsed___errors__ops/sec(inst)___ops/sec(cum)__p50(ms)__p95(ms)__p99(ms)_pMax(ms)
         1s        0         1443.4         1494.8      4.7      9.4     27.3     67.1 transfer
         2s        0         1686.5         1590.9      4.7      8.1     15.2     28.3 transfer
         3s        0         1735.7         1639.0      4.7      7.3     11.5     28.3 transfer
         4s        0         1542.6         1614.9      5.0      8.9     12.1     21.0 transfer
         5s        0         1695.9         1631.1      4.7      7.3     11.5     22.0 transfer
         6s        0         1569.2         1620.8      5.0      8.4     11.5     15.7 transfer
         7s        0         1614.6         1619.9      4.7      8.1     12.1     16.8 transfer
         8s        0         1344.4         1585.6      5.8     10.0     15.2     31.5 transfer
         9s        0         1351.9         1559.5      5.8     10.0     16.8     54.5 transfer
        10s        0         1514.8         1555.0      5.2      8.1     12.1     16.8 transfer
   ...
   

   After the specified duration (10 minutes in this case), the workload will stop and you'll see totals printed to standard output:

   _elapsed___errors_____ops(total)___ops/sec(cum)__avg(ms)__p50(ms)__p95(ms)__p99(ms)_pMax(ms)__result
     600.0s        0         823902         1373.2      5.8      5.5     10.0     15.2    209.7
   

   Tip:

   For more tpcc options, use cockroach workload run tpcc --help. For details about other workloads built into the cockroach binary, use cockroach workload --help.

6. To monitor the load generator's progress, open the DB Console by pointing a browser to the address in the admin field in the standard output of any node on startup.

   Since the load generator is pointed at the load balancer, the connections will be evenly distributed across nodes. To verify this, click Metrics on the left, select the SQL dashboard, and then check the SQL Connections graph. You can use the Graph menu to filter the graph for specific nodes.

Step 7. Monitor the cluster

Despite CockroachDB's various built-in safeguards against failure, it is critical to actively monitor the overall health and performance of a cluster running in production and to create alerting rules that promptly send notifications when there are events that require investigation or intervention.

For details about available monitoring options and the most important events and metrics to alert on, see Monitoring and Alerting.

Step 8. Scale the cluster

You can start the nodes manually or automate the process using systemd.

Step 9. Use the cluster

Now that your deployment is working, you can:

1. Implement your data model.
2. Create users and grant them privileges.
3. Connect your application. Be sure to connect your application to the load balancer, not to a CockroachDB node.
4. Take backups of your data.

You may also want to adjust the way the cluster replicates data. For example, by default, a multi-node cluster replicates all data 3 times; you can change this replication factor or create additional rules for replicating individual databases and tables differently. For more information, see Replication Controls.

See also

• Production Checklist
• Manual Deployment
• Orchestrated Deployment
• Monitoring and Alerting
• Performance Benchmarking
• Performance Tuning
• Local Deployment

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