在過去 28 天，我們深入探討了 ClickHouse 的內部設計，包括 MergeTree 引擎、索引、查詢優化技巧，以及不同引擎在資料處理上的應用。這些都屬於「單機或單節點」的觀點。然而，當我們要把 ClickHouse 放到真實的生產環境，面對高併發、資料量成長、以及高可用性的需求時，部署策略就變得格外重要。
傳統部署方式（例如直接在 VM 或裸機上安裝）雖然簡單，但在現代雲端架構下已經不足以滿足需求。Kubernetes 作為事實上的容器編排標準，提供了自動化、可擴展、彈性化的能力，而 ClickHouse Operator 則讓我們能輕鬆在 Kubernetes 上管理複雜的 ClickHouse 叢集。
本篇文章會透過在單一主機上快速使用 minikube 和 clickhouse operator 模擬 Kubernates 部署分散式架構。
ClickHouse 本身的設計就非常快，但隨著資料量和使用者數成長，單一節點往往無法承受所有負載。我們需要：
ClickHouse Operator 的出現，就是為了解決這些問題。
ClickHouse Operator 是由 Altinity 與開源社群維護的 Kubernetes Operator，主要目標是簡化 ClickHouse 叢集的管理。
它的主要功能包括：
結構上，Operator 會監控 Kubernetes 中的 ClickHouseCluster 資源，一旦偵測到改動（例如新增一個 replica），就會自動調整底層 StatefulSet 與 Pod，確保叢集狀態與宣告式配置一致。
在雲端上，我們通常會設計一個具備以下特性的架構：
emptyDir (作為 Demo, 關掉後就會自動刪除資料)架構圖示例：
圖好像有點大...
這樣的設計能確保：
建立分散式表之前有幾項前置作業：
minikube dashboard 開啟 GUI 介面~ (Optional)kubectl apply -f https://raw.githubusercontent.com/Altinity/clickhouse-operator/master/deploy/operator/clickhouse-operator-install-bundle.yaml
kubectl create namespace zoo3ns
# zookeeper-3-nodes.yaml
# Setup Service to provide access to Zookeeper for clients
apiVersion: v1
kind: Service
metadata:
# DNS would be like zookeeper.zoons
name: zookeeper
labels:
app: zookeeper
spec:
ports:
- port: 2181
name: client
- port: 7000
name: prometheus
selector:
app: zookeeper
what: node
---
# Setup Headless Service for StatefulSet
apiVersion: v1
kind: Service
metadata:
# DNS would be like zookeeper-0.zookeepers.etc
name: zookeepers
labels:
app: zookeeper
spec:
ports:
- port: 2888
name: server
- port: 3888
name: leader-election
clusterIP: None
selector:
app: zookeeper
what: node
---
# Setup max number of unavailable pods in StatefulSet
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
name: zookeeper-pod-disruption-budget
spec:
selector:
matchLabels:
app: zookeeper
maxUnavailable: 1
---
# Setup Zookeeper StatefulSet
# Possible params:
# 1. replicas
# 2. memory
# 3. cpu
# 4. storage
# 5. storageClassName
# 6. user to run app
apiVersion: apps/v1
kind: StatefulSet
metadata:
# nodes would be named as zookeeper-0, zookeeper-1, zookeeper-2
name: zookeeper
labels:
app: zookeeper
spec:
selector:
matchLabels:
app: zookeeper
serviceName: zookeepers
replicas: 3
updateStrategy:
type: RollingUpdate
podManagementPolicy: OrderedReady
template:
metadata:
labels:
app: zookeeper
what: node
annotations:
prometheus.io/port: '7000'
prometheus.io/scrape: 'true'
spec:
affinity: {}
containers:
- name: kubernetes-zookeeper
imagePullPolicy: IfNotPresent
image: "docker.io/zookeeper:3.8.4"
resources:
requests:
memory: "512M"
cpu: "1"
limits:
memory: "4Gi"
cpu: "2"
ports:
- containerPort: 2181
name: client
- containerPort: 2888
name: server
- containerPort: 3888
name: leader-election
- containerPort: 7000
name: prometheus
env:
- name: SERVERS
value: "3"
# See those links for proper startup settings:
# https://github.com/kow3ns/kubernetes-zookeeper/blob/master/docker/scripts/start-zookeeper
# https://clickhouse.yandex/docs/en/operations/tips/#zookeeper
# https://github.com/ClickHouse/ClickHouse/issues/11781
command:
- bash
- -x
- -c
- |
HOST=`hostname -s` &&
DOMAIN=`hostname -d` &&
CLIENT_PORT=2181 &&
SERVER_PORT=2888 &&
ELECTION_PORT=3888 &&
PROMETHEUS_PORT=7000 &&
ZOO_DATA_DIR=/var/lib/zookeeper/data &&
ZOO_DATA_LOG_DIR=/var/lib/zookeeper/datalog &&
{
echo "clientPort=${CLIENT_PORT}"
echo 'tickTime=2000'
echo 'initLimit=300'
echo 'syncLimit=10'
echo 'maxClientCnxns=2000'
echo 'maxTimeToWaitForEpoch=2000'
echo 'maxSessionTimeout=60000000'
echo "dataDir=${ZOO_DATA_DIR}"
echo "dataLogDir=${ZOO_DATA_LOG_DIR}"
echo 'autopurge.snapRetainCount=10'
echo 'autopurge.purgeInterval=1'
echo 'preAllocSize=131072'
echo 'snapCount=3000000'
echo 'leaderServes=yes'
echo 'standaloneEnabled=false'
echo '4lw.commands.whitelist=*'
echo 'metricsProvider.className=org.apache.zookeeper.metrics.prometheus.PrometheusMetricsProvider'
echo "metricsProvider.httpPort=${PROMETHEUS_PORT}"
echo "skipACL=true"
echo "fastleader.maxNotificationInterval=10000"
} > /conf/zoo.cfg &&
{
echo "zookeeper.root.logger=CONSOLE"
echo "zookeeper.console.threshold=INFO"
echo "log4j.rootLogger=\${zookeeper.root.logger}"
echo "log4j.appender.CONSOLE=org.apache.log4j.ConsoleAppender"
echo "log4j.appender.CONSOLE.Threshold=\${zookeeper.console.threshold}"
echo "log4j.appender.CONSOLE.layout=org.apache.log4j.PatternLayout"
echo "log4j.appender.CONSOLE.layout.ConversionPattern=%d{ISO8601} - %-5p [%t:%C{1}@%L] - %m%n"
} > /conf/log4j.properties &&
echo 'JVMFLAGS="-Xms128M -Xmx4G -XX:ActiveProcessorCount=8 -XX:+AlwaysPreTouch -Djute.maxbuffer=8388608 -XX:MaxGCPauseMillis=50"' > /conf/java.env &&
if [[ $HOST =~ (.*)-([0-9]+)$ ]]; then
NAME=${BASH_REMATCH[1]} &&
ORD=${BASH_REMATCH[2]};
else
echo "Failed to parse name and ordinal of Pod" &&
exit 1;
fi &&
mkdir -pv ${ZOO_DATA_DIR} &&
mkdir -pv ${ZOO_DATA_LOG_DIR} &&
whoami &&
chown -Rv zookeeper "$ZOO_DATA_DIR" "$ZOO_DATA_LOG_DIR" &&
export MY_ID=$((ORD+1)) &&
echo $MY_ID > $ZOO_DATA_DIR/myid &&
for (( i=1; i<=$SERVERS; i++ )); do
echo "server.$i=$NAME-$((i-1)).$DOMAIN:$SERVER_PORT:$ELECTION_PORT" >> /conf/zoo.cfg;
done &&
if [[ $SERVERS -eq 1 ]]; then
echo "group.1=1" >> /conf/zoo.cfg;
else
echo "group.1=1:2:3" >> /conf/zoo.cfg;
fi &&
for (( i=1; i<=$SERVERS; i++ )); do
WEIGHT=1
if [[ $i == 1 ]]; then
WEIGHT=10
fi
echo "weight.$i=$WEIGHT" >> /conf/zoo.cfg;
done &&
zkServer.sh start-foreground
readinessProbe:
exec:
command:
- bash
- -c
- '
IFS=;
MNTR=$(exec 3<>/dev/tcp/127.0.0.1/2181 ; printf "mntr" >&3 ; tee <&3; exec 3<&- ;);
while [[ "$MNTR" == "This ZooKeeper instance is not currently serving requests" ]];
do
echo "wait mntr works";
sleep 1;
MNTR=$(exec 3<>/dev/tcp/127.0.0.1/2181 ; printf "mntr" >&3 ; tee <&3; exec 3<&- ;);
done;
STATE=$(echo -e $MNTR | grep zk_server_state | cut -d " " -f 2);
if [[ "$STATE" =~ "leader" ]]; then
echo "check leader state";
SYNCED_FOLLOWERS=$(echo -e $MNTR | grep zk_synced_followers | awk -F"[[:space:]]+" "{print \$2}" | cut -d "." -f 1);
if [[ "$SYNCED_FOLLOWERS" != "0" ]]; then
./bin/zkCli.sh ls /;
exit $?;
else
exit 0;
fi;
elif [[ "$STATE" =~ "follower" ]]; then
echo "check follower state";
PEER_STATE=$(echo -e $MNTR | grep zk_peer_state);
if [[ "$PEER_STATE" =~ "following - broadcast" ]]; then
./bin/zkCli.sh ls /;
exit $?;
else
exit 1;
fi;
else
exit 1;
fi
'
initialDelaySeconds: 15
periodSeconds: 10
timeoutSeconds: 60
livenessProbe:
exec:
command:
- bash
- -xc
- 'date && OK=$(exec 3<>/dev/tcp/127.0.0.1/2181 ; printf "ruok" >&3 ; IFS=; tee <&3; exec 3<&- ;); if [[ "$OK" == "imok" ]]; then exit 0; else exit 1; fi'
initialDelaySeconds: 10
periodSeconds: 30
timeoutSeconds: 5
volumeMounts:
- name: datadir-volume
mountPath: /var/lib/zookeeper
# Run as a non-privileged user
securityContext:
runAsUser: 1000
fsGroup: 1000
volumes:
- name: datadir-volume
emptyDir:
medium: "" #accepted values: empty str (means node's default medium) or Memory
sizeLimit: 1Gi
接著 apply 該部署文件和確認 zookeeper 是否都已建立：
# 部署配置
kubectl apply -f zookeeper-3-nodes.yaml -n zoo3ns
# 確認 service
kubectl get svc -n zoo3ns
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
zookeeper ClusterIP {YOUR-CLUSTER-IP} <none> 2181/TCP,7000/TCP 54m
zookeepers ClusterIP None <none> 2888/TCP,3888/TCP 54m
# 確認 pods
kubectl get pod -n zoo3ns
NAME READY STATUS RESTARTS AGE
zookeeper-0 1/1 Running 0 53m
zookeeper-1 1/1 Running 0 53m
zookeeper-2 1/1 Running 0 52m
# clickhouse-1shards-2replicas.yaml
apiVersion: "clickhouse.altinity.com/v1"
kind: "ClickHouseInstallation"
metadata:
name: "repl-05"
spec:
defaults:
templates:
dataVolumeClaimTemplate: default
podTemplate: clickhouse-20.7
configuration:
zookeeper:
nodes:
- host: zookeeper.zoo3ns
clusters:
- name: replicated
layout:
shardsCount: 1
replicasCount: 2
templates:
volumeClaimTemplates:
- name: default
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 500Mi
podTemplates:
- name: clickhouse-20.7
spec:
containers:
- name: clickhouse-pod
image: clickhouse/clickhouse-server:24.8
接著 apply 該部署文件和確認 zookeeper 是否都已建立：
# 部署配置
kubectl apply -f clickhouse-1shards-2replicas.yaml -n zoo3ns
# 確認 service
kubectl get svc -n zoo3ns
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
chi-repl-05-replicated-0-0 ClusterIP None <none> 9000/TCP,8123/TCP,9009/TCP 50m
chi-repl-05-replicated-0-1 ClusterIP None <none> 9000/TCP,8123/TCP,9009/TCP 49m
clickhouse-repl-05 ClusterIP None <none> 8123/TCP,9000/TCP 49m
# 確認 pods
kubectl get pod -n zoo3ns
NAME READY STATUS RESTARTS AGE
chi-repl-05-replicated-0-0-0 1/1 Running 0 50m
chi-repl-05-replicated-0-1-0 1/1 Running 0 50m
如果都完成了，恭喜你完成最難的一步🚀：建置環境
進入 ClickHouse 內部測試是否成功
kubectl exec -it chi-repl-05-replicated-0-0-0 -- bash
kubectl exec -it chi-repl-05-replicated-0-1-0 -- bash
clickhouse-client
chi-repl-05-replicated-0-0-0 pod 內部建立 ReplicatedMergeTree，這個 MergeTree 引擎可以幫助你自動同步不同 cluster, shards, replicas... 的資料CREATE TABLE events_local ON CLUSTER `{cluster}`
(
`event_date` Date,
`event_type` Int32,
`article_id` Int32,
`title` String
)
ENGINE = ReplicatedMergeTree('/clickhouse/{installation}/{cluster}/tables/{shard}/{database}/{table}', '{replica}')
PARTITION BY toYYYYMM(event_date)
ORDER BY (event_type, article_id)
得到結果，代表你已經成功新增了。
Query id: 0e9d3beb-59ea-4194-9dbe-9f7cf88e19cc
┌─host───────────────────────┬─port─┬─status─┬─error─┬─num_hosts_remaining─┬─num_hosts_active─┐
1. │ chi-repl-05-replicated-0-0 │ 9000 │ 0 │ │ 1 │ 0 │
2. │ chi-repl-05-replicated-0-1 │ 9000 │ 0 │ │ 0 │ 0 │
└────────────────────────────┴──────┴────────┴───────┴─────────────────────┴──────────────────┘
2 rows in set. Elapsed: 0.253 sec.
CREATE TABLE events ON CLUSTER `{cluster}` AS events_local
ENGINE = Distributed('{cluster}', default, events_local, rand())
得到結果，代表你已經成功新增了。
Query id: b203ec4b-08b1-45bf-98ea-6d4ad32956d8
┌─host───────────────────────┬─port─┬─status─┬─error─┬─num_hosts_remaining─┬─num_hosts_active─┐
1. │ chi-repl-05-replicated-0-0 │ 9000 │ 0 │ │ 1 │ 0 │
2. │ chi-repl-05-replicated-0-1 │ 9000 │ 0 │ │ 0 │ 0 │
└────────────────────────────┴──────┴────────┴───────┴─────────────────────┴──────────────────┘
2 rows in set. Elapsed: 0.084 sec.
chi-repl-05-replicated-0-0-0 插入資料，在 chi-repl-05-replicated-0-1-0 觀察資料是否有同步
chi-repl-05-replicated-0-1-0 觀察，沒有資料是正常的:SELECT *
FROM events_local
WHERE event_type = 100
Query id: 4dd7fbbd-4089-4b7a-aa16-af78baeaf3f4
Ok.
0 rows in set. Elapsed: 0.002 sec.
chi-repl-05-replicated-0-0-0 插入資料INSERT INTO events VALUES (today(), 100, 123, 'from pod A');
chi-repl-05-replicated-0-1-0 觀察:SELECT *
FROM events_local
WHERE event_type = 100
Query id: 1537f542-a13d-4a19-b29b-baed69b476c8
┌─event_date─┬─event_type─┬─article_id─┬─title──────┐
1. │ 2025-08-28 │ 100 │ 123 │ from pod A │
└────────────┴────────────┴────────────┴────────────┘
1 row in set. Elapsed: 0.002 sec.
到這邊都是正確的，代表你成功了！！！（但是是在單節點上）
即便有了 Operator，仍然會遇到一些挑戰：
儲存管理
升級策略
監控與觀測性
網路與流量分配
| 面向 | VM/裸機部署 | Kubernetes 部署 |
|---|---|---|
| 部署方式 | 手動安裝、配置 | YAML 定義、自動化 |
| 擴容 | 需人工加機器、改設定 | 修改 replicas/shards 即可 |
| 高可用 | 需人工維護 Zookeeper | Operator 自動協調 |
| 升級 | 容易停機 | 滾動更新、零停機 |
| 監控 | 額外安裝 | Prometheus/Grafana 整合 |
結論很明顯：如果你是單機測試，VM 部署即可；但若要進入生產環境，Kubernetes + Operator 幾乎是標準解。
ClickHouse 本身非常強大，但若缺乏好的部署方式，容易因節點故障、擴展困難、升級不便而影響穩定性。Kubernetes 與 ClickHouse Operator 的結合，讓我們能：
在資料量不斷成長的今天，這種雲原生部署方式已成為 ClickHouse 生產環境的首選。
明天就是 ClickHouse 系列最後一天了：Ｄ
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