Deploy CockroachDB on Microsoft Azure (Insecure)

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This page shows you how to manually deploy an insecure multi-node CockroachDB cluster on Microsoft Azure, using Azure's managed load balancing service to distribute client traffic.

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 Admin UI

Recommendations

• If you plan to use CockroachDB in production, carefully review the Production Checklist.

• 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 Admin UI:

  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 Admin UI.

Step 1. Configure your network

CockroachDB requires TCP communication on two ports:

• 26257 (tcp:26257) for inter-node communication (i.e., working as a cluster), for applications to connect to the load balancer, and for routing from the load balancer to nodes
• 8080 (tcp:8080) for exposing your Admin UI

To enable this in Azure, you must create a Resource Group, Virtual Network, and Network Security Group.

1. Create a Resource Group.

2. Create a Virtual Network that uses your Resource Group.

3. Create a Network Security Group that uses your Resource Group, and then add the following inbound rules to it:

   • Admin UI support:

     Field	Recommended Value
     Name	cockroachadmin
     Source	IP Addresses
     Source IP addresses/CIDR ranges	Your local network’s IP ranges
     Source port ranges	*
     Destination	Any
     Destination port range	8080
     Protocol	TCP
     Action	Allow
     Priority	Any value > 1000

   • Application support:

     Tip:

     If your application is also hosted on the same Azure Virtual Network, you will not need to create a firewall rule for your application to communicate with your load balancer.

     Field	Recommended Value
     Name	cockroachapp
     Source	IP Addresses
     Source IP addresses/CIDR ranges	Your local network’s IP ranges
     Source port ranges	*
     Destination	Any
     Destination port range	26257
     Protocol	TCP
     Action	Allow
     Priority	Any value > 1000

Step 2. Create VMs

Create Linux VMs for each node you plan to have in your cluster. If you plan to run a sample workload against the cluster, create a separate VM for that workload.

• Run at least 3 nodes to ensure survivability.

• Use storage-optimized Ls-series VMs with Premium Storage or local SSD storage with a Linux filesystem such as ext4 (not the Windows ntfs filesystem). For example, Cockroach Labs has used Standard_L4s VMs (4 vCPUs and 32 GiB of RAM per VM) for internal testing.

  • If you choose local SSD storage, on reboot, the VM can come back with the ntfs filesystem. Be sure your automation monitors for this and reformats the disk to the Linux filesystem you chose initially.

• Do not use "burstable" B-series VMs, which limit the load on a single core. Also, Cockroach Labs has experienced data corruption issues on A-series VMs and irregular disk performance on D-series VMs, so we recommend avoiding those as well.

• When creating the VMs, make sure to select the Resource Group, Virtual Network, and Network Security Group you created.

For more details, see Hardware Recommendations and Cluster Topology.

Step 3. 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. However, to run ntpd properly on Azure VMs, it's necessary to first unbind the Time Synchronization device used by the Hyper-V technology running Azure VMs; this device aims to synchronize time between the VM and its host operating system but has been known to cause problems.

1. SSH to the first machine.

2. Find the ID of the Hyper-V Time Synchronization device:

   $ curl -O https://raw.githubusercontent.com/torvalds/linux/master/tools/hv/lsvmbus
   

   $ python lsvmbus -vv | grep -w "Time Synchronization" -A 3
   

   VMBUS ID 12: Class_ID = {9527e630-d0ae-497b-adce-e80ab0175caf} - [Time Synchronization]
       Device_ID = {2dd1ce17-079e-403c-b352-a1921ee207ee}
       Sysfs path: /sys/bus/vmbus/devices/2dd1ce17-079e-403c-b352-a1921ee207ee
       Rel_ID=12, target_cpu=0
   

3. Unbind the device, using the Device_ID from the previous command's output:

   $ echo <DEVICE_ID> | sudo tee /sys/bus/vmbus/drivers/hv_util/unbind
   

4. Install the ntp package:

   $ sudo apt-get install ntp
   

5. Stop the NTP daemon:

   $ sudo service ntp stop
   

6. 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 they handle "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.

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

   $ sudo ntpq -p
   

   The active NTP server will be marked with an asterisk.

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

Step 4. 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.

Microsoft Azure offers fully-managed load balancing to distribute traffic between instances.

1. Add Azure load balancing. Be sure to:

   • Set forwarding rules to route TCP traffic from the load balancer's port 26257 to port 26257 on the nodes.
   • Configure health checks to use HTTP port 8080 and path /health?ready=1. This health endpoint ensures that load balancers do not direct traffic to nodes that are live but not ready to receive requests.

2. Note the provisioned IP Address for the load balancer. You'll use this later to test load balancing and to connect your application to the cluster.

Step 5. Start nodes

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

Step 6. 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>
   

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

Step 7. Test the cluster

CockroachDB replicates and distributes data for you behind-the-scenes and uses a Gossip protocol to enable each node to locate data across the cluster.

To test this, 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 any node:

   $ cockroach sql --insecure --host=<address of any node>
   

2. Create an insecurenodetest database:

   > CREATE DATABASE insecurenodetest;
   

3. Use \q or ctrl-d to exit the SQL shell.

4. Launch the built-in SQL client, with the --host flag set to the address of a different node:

   $ cockroach sql --insecure --host=<address of different node>
   

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

   > SHOW DATABASES;
   

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

6. Use \q or ctrl-d to exit the SQL shell.

Step 8. Run a sample workload

CockroachDB offers a pre-built workload binary for Linux that includes several load generators for simulating client traffic against your cluster. 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 workload and make it executable:

   $ wget https://edge-binaries.cockroachdb.com/cockroach/workload.LATEST ; chmod 755 workload.LATEST
   

3. Rename and copy workload into the PATH:

   $ cp -i workload.LATEST /usr/local/bin/workload
   

4. Start the TPC-C workload, pointing it at the IP address of the load balancer:

   $ workload run tpcc \
   --drop \
   --init \
   --duration=20m \
   --tolerate-errors \
   "postgresql://root@<IP ADDRESS OF LOAD BALANCER:26257/tpcc?sslmode=disable"
   

   This command runs the TPC-C workload against the cluster for 20 minutes, loading 1 "warehouse" of data initially and then issuing about 12 queries per minute via 10 "worker" threads. These workers share SQL connections since individual workers are idle for long periods of time between queries.

   Tip:

   For more tpcc options, use workload run tpcc --help. For details about other load generators included in workload, use workload run --help.

5. To monitor the load generator's progress, open the Admin UI 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 9. Set up monitoring and alerting

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 10. Scale the cluster

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

Step 11. 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 Azure load balancer, not to a CockroachDB node.

See Also

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

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