Skip to main content

Deploying a Cluster on Kubernetes

This topic explains how to install and configure the Databend cluster on Kubernetes.

Before You Begin

  • Ensure helm command installed, see guide

  • Make sure you have a Kubernetes cluster up and running. For example:

    Also, there are simple Kubernetes Engines for local testing:

  • Create a Cloud Object Storage with corresponding credentials, i.e., access_key_id and secret_access_key.

    • AWS S3 or other S3 compatible storage service
    • Azure Storage Blob
    • Other storage services supported by Apache OpenDAL
    For advanced user

    Authentication methods without access keys are also supported:

  • Ensure there is a default storage class for the Kubernetes cluster.

    For cloud platforms

    Amazon Elastic Block Store (EBS) CSI driver is recommended. And remember to set the annotation for default class when adding storage classes, for example:

    storageClasses:
    - name: gp3
    annotations:
    storageclass.kubernetes.io/is-default-class: "true"
    allowVolumeExpansion: true
    volumeBindingMode: WaitForFirstConsumer
    reclaimPolicy: Delete
    parameters:
    type: gp3
    ❯ kubectl get sc
    NAME PROVISIONER RECLAIMPOLICY VOLUMEBINDINGMODE ALLOWVOLUMEEXPANSION AGE
    gp2 kubernetes.io/aws-ebs Delete WaitForFirstConsumer true 16d
    gp3 (default) ebs.csi.aws.com Delete WaitForFirstConsumer true 15d
  • Recommended Ensure Prometheus Operator running in Kubernetes cluster, if you want to monitor the status for Databend Meta and Databend Query.

    Steps for a simple Kube Prometheus Stack
    1. Add chart repository for kube-prometheus-stack

      helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
      helm repo update prometheus-community
    2. Prepare a values file for simple kube-prometheus-stack installation

      values.yaml
      grafana:
      grafana.ini:
      auth.anonymous:
      enabled: true
      org_role: Admin
      prometheus:
      prometheusSpec:
      ruleNamespaceSelector: {}
      ruleSelectorNilUsesHelmValues: false
      serviceMonitorNamespaceSelector: {}
      serviceMonitorSelectorNilUsesHelmValues: false
      podMonitorNamespaceSelector: {}
      podMonitorSelectorNilUsesHelmValues: false
    3. Install Kube Prometheus Stack with helm

      helm upgrade --install monitoring \
      prometheus-community/kube-prometheus-stack \
      --namespace monitoring \
      --create-namespace \
      --values values.yaml
    4. Verify prometheus & grafana running

      ❯ kubectl -n monitoring get pods
      NAME READY STATUS RESTARTS AGE
      monitoring-prometheus-node-exporter-7km6w 1/1 Running 0 19m
      monitoring-kube-prometheus-operator-876c99fb8-qjnpd 1/1 Running 0 19m
      monitoring-kube-state-metrics-7c9f7fc49b-4884t 1/1 Running 0 19m
      alertmanager-monitoring-kube-prometheus-alertmanager-0 2/2 Running 1 (18m ago) 18m
      monitoring-grafana-654b4bb58c-sf9wp 3/3 Running 0 19m
      prometheus-monitoring-kube-prometheus-prometheus-0 2/2 Running 0 18m

Deploy a Sample Databend Cluster

Step 1. Deploy a Databend Meta Cluster

  1. Create a values file with persistent and monitoring enabled:

Detailed and default values are available at documentation

values.yaml
bootstrap: true
replicaCount: 3
persistence:
size: 20Gi
serviceMonitor:
enabled: true
caution

It is highly recommended to deploy an at least 3-nodes cluster with persistent storage on each node for high availability.

When replicaCount > 1, a bootstrap: true is necessary on first run, and could be removed when all nodes in cluster are up and running.

  1. Deploy the meta cluster in namespace databend-meta
helm repo add databend https://charts.databend.rs
helm repo update databend

helm upgrade --install databend-meta databend/databend-meta \
--namespace databend-meta --create-namespace \
--values values.yaml
  1. Wait and verify meta service running
❯ kubectl -n databend-meta get pods
NAME READY STATUS RESTARTS AGE
databend-meta-0 1/1 Running 0 5m36s
databend-meta-1 1/1 Running 1 (4m38s ago) 4m53s
databend-meta-2 1/1 Running 1 (4m2s ago) 4m18s

❯ kubectl -n databend-meta get pvc
NAME STATUS VOLUME CAPACITY ACCESS MODES STORAGECLASS AGE
data-databend-meta-0 Bound pvc-578ec207-bf7e-4bac-a9a1-3f0e4b140b8d 20Gi RWO local-path 5m45s
data-databend-meta-1 Bound pvc-693a0350-6b87-491d-8575-90bf62179b59 20Gi RWO local-path 5m2s
data-databend-meta-2 Bound pvc-08bd4ceb-15c2-47f3-a637-c1cc10441874 20Gi RWO local-path 4m27s

Step 2. Deploy a Databend Query Cluster

  1. Create a values file with builtin user databend:databend and cluster name example_cluster with 3 nodes.

Detailed and default values are available at documentation

replicaCount: 3
config:
query:
clusterId: example_cluster
# add builtin user
users:
- name: databend
# available type: sha256_password, double_sha1_password, no_password, jwt
authType: double_sha1_password
# echo -n "databend" | sha1sum | cut -d' ' -f1 | xxd -r -p | sha1sum
authString: 3081f32caef285c232d066033c89a78d88a6d8a5
meta:
# Set endpoints to use remote meta service
# depends on previous deployed meta service、namespace and nodes
endpoints:
- "databend-meta-0.databend-meta.databend-meta.svc:9191"
- "databend-meta-1.databend-meta.databend-meta.svc:9191"
- "databend-meta-2.databend-meta.databend-meta.svc:9191"
storage:
# s3, oss
type: s3
s3:
bucket: "<bucket>"
region: "<region>"
access_key_id: "<key>"
secret_access_key: "<secret>"
root: ""
# [recommended] enable monitoring service
serviceMonitor:
enabled: true
# [recommended] enable access from outside cluster
service:
type: LoadBalancer
for LoadBalancer

When setting the service type to LoadBalancer, almost all cloud platform would assign a public ip address for the query service, this may lead to security problem.

Then annotations would be necessary to tell the cloud platform create an internal loadbalancer.

For different cloud providers:

Recommended to have AWS Load Balancer Controller installed.

service:
type: LoadBalancer
annotations:
service.beta.kubernetes.io/aws-load-balancer-type: external
service.beta.kubernetes.io/aws-load-balancer-nlb-target-type: ip
service.beta.kubernetes.io/aws-load-balancer-scheme: internal
for cloud storage
config:
storage:
type: s3
s3:
# default endpoint
endpoint_url: "s3.amazonaws.com"
bucket: "<bucket>"
region: "<region>"
access_key_id: "<key>"
secret_access_key: "<secret>"
root: ""
  1. Deploy the query cluster for tenant1 in namespace databend-query
helm repo add databend https://charts.databend.rs
helm repo update databend

helm upgrade --install tenant1 databend/databend-query \
--namespace databend-query --create-namespace \
--values values.yaml
  1. Wait and verify query service running
❯ kubectl -n databend-query get pods
NAME READY STATUS RESTARTS AGE
tenant1-databend-query-66647594c-lkkm9 1/1 Running 0 36s
tenant1-databend-query-66647594c-lpl2s 1/1 Running 0 36s
tenant1-databend-query-66647594c-4hlpw 1/1 Running 0 36s

❯ kubectl -n databend-query get svc
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
tenant1-databend-query LoadBalancer 10.43.84.243 172.20.0.2 8080:32063/TCP,9000:31196/TCP,9090:30472/TCP,8000:30050/TCP,7070:31253/TCP,3307:31367/TCP 17m
  1. Access the query cluster

    We use the builtin user databend here:

  • in-cluster access

    mysql -htenant1-databend-query.databend-query.svc -udatabend -P3307 -pdatabend
  • outside-cluster access with loadbalancer

    # the address here is the `EXTERNAL-IP` for service tenant1-databend-query above
    mysql -h172.20.0.2 -udatabend -P3307 -pdatabend
  • local access with kubectl

    nohup kubectl port-forward -n databend-query svc/tenant1-databend-query 3307:3307 &
    mysql -h127.0.0.1 -udatabend -P3307 -pdatabend
  1. Deploy a second cluster for tenant2

modify the values.yaml for tenant2

# optional
helm repo update databend

helm upgrade --install tenant2 databend/databend-query \
--namespace databend-query --create-namespace \
--values values.yaml
Verify the query service for tenant2 running
❯ kubectl -n databend-query get pods
NAME READY STATUS RESTARTS AGE
tenant1-databend-query-66647594c-lkkm9 1/1 Running 0 55m
tenant1-databend-query-66647594c-lpl2s 1/1 Running 0 55m
tenant1-databend-query-66647594c-4hlpw 1/1 Running 0 55m
tenant2-databend-query-59dcc4949f-9qg9b 1/1 Running 0 53s
tenant2-databend-query-59dcc4949f-pfxxj 1/1 Running 0 53s
tenant2-databend-query-59dcc4949f-mmwr9 1/1 Running 0 53s

Maintain Databend Query Cluster

Scale

to scale up or down the query cluster, there are two ways

  • directly use kubectl

     # scale query cluster number to 0
    kubectl -n databend-query scale deployment tenant1-databend-query --replicas=0

    # scale query cluster number to 5
    kubectl -n databend-query scale deployment tenant1-databend-query --replicas=5
  • update replicaCount in values.yaml to any value, then helm upgrade again

    diff values.yaml
    - replicaCount: 3
    + replicaCount: 5
    helm upgrade --install tenant1 databend/databend-query \
    --namespace databend-query --create-namespace \
    --values values.yaml

Upgrade

to upgrade the query cluster, we need to modify the values.yaml for query cluster above.

diff values.yaml
replicaCount: 3
+ image:
+ tag: "v0.8.123-nightly"
config:
query:
clusterId: example_cluster

then just run again helm upgrade

# optional
helm repo update databend

helm upgrade --install tenant1 databend/databend-query \
--namespace databend-query --create-namespace \
--values values.yaml

Check the Cluster Information

MySQL [(none)]> select * from system.clusters;
+------------------------+------------+------+------------------------------------------------------------------------------+
| name | host | port | version |
+------------------------+------------+------+------------------------------------------------------------------------------+
| TJoPIFqvwU6l6IuZzwVmj | 10.42.0.29 | 9090 | v0.8.122-nightly-5d3a308(rust-1.67.0-nightly-2022-11-20T16:27:23.284298522Z) |
| e7leCg352OPa7bIBTi3ZK | 10.42.0.30 | 9090 | v0.8.122-nightly-5d3a308(rust-1.67.0-nightly-2022-11-20T16:27:23.284298522Z) |
| uGD38DVaWDAnJV5jupK4p4 | 10.42.0.28 | 9090 | v0.8.122-nightly-5d3a308(rust-1.67.0-nightly-2022-11-20T16:27:23.284298522Z) |
+------------------------+------------+------+------------------------------------------------------------------------------+
3 rows in set (0.009 sec)

Verify Distributed Query Working

MySQL [(none)]> EXPLAIN SELECT max(number), sum(number) FROM numbers_mt(10000000000) GROUP BY number % 3, number % 4, number % 5 LIMIT 10;
+-------------------------------------------------------------------------------------------------------------------------------------------+
| explain |
+-------------------------------------------------------------------------------------------------------------------------------------------+
| Limit |
| ├── limit: 10 |
| ├── offset: 0 |
| └── Exchange |
| ├── exchange type: Merge |
| └── EvalScalar |
| ├── expressions: [max(number) (#6), sum(number) (#7)] |
| └── AggregateFinal |
| ├── group by: [number % 3, number % 4, number % 5] |
| ├── aggregate functions: [max(number), sum(number)] |
| └── Exchange |
| ├── exchange type: Hash(_group_by_key) |
| └── AggregatePartial |
| ├── group by: [number % 3, number % 4, number % 5] |
| ├── aggregate functions: [max(number), sum(number)] |
| └── EvalScalar |
| ├── expressions: [%(numbers_mt.number (#0), 3), %(numbers_mt.number (#0), 4), %(numbers_mt.number (#0), 5)] |
| └── TableScan |
| ├── table: default.system.numbers_mt |
| ├── read rows: 10000000000 |
| ├── read bytes: 80000000000 |
| ├── partitions total: 152588 |
| ├── partitions scanned: 152588 |
| └── push downs: [filters: [], limit: NONE] |
+-------------------------------------------------------------------------------------------------------------------------------------------+
24 rows in set (0.008 sec)

The distributed query works, and the cluster will efficiently transfer data through flight_api_address.

Upload Data to the Cluster

CREATE TABLE t1(i INT, j INT);
INSERT INTO t1 SELECT number, number + 300 from numbers(10000000);
SELECT count(*) FROM t1;
+----------+
| count() |
+----------+
| 10000000 |
+----------+

Monitoring the Meta and Query cluster

info

Note the serviceMonitor should be enabled when deploying meta and query cluster.

  • Download the grafana dashboard files from: datafuselabs/helm-charts.

  • Open grafana web for your cluster.

  • Select + Import on the left sidebar, and upload the downloaded two json files.

  • Then you should see the two dashboard:

    • Databend Meta Runtime
    • Databend Query Runtime

Next Steps

After deploying Databend, you might need to learn about the following topics: