Sentry 雲原生中間件事件 ClickHouse PaaS ，為 Snuba 引擎提供動力

目錄(腦圖)

ClickHouse PaaS 雲原生多租戶平台(Altinity.Cloud)

官網：https://altinity.cloud

PaaS 架構概覽

設計一個擁有雲原生編排能力、支援多雲環境部署、自動化運維、彈性擴縮容、故障自愈等特性，同時提供租戶隔離、權限管理、操作審計等企業級能力的高性能、低成本的分布式中間件服務是真挺難的。

SaaS 模式傳遞給使用者

Sentry Snuba 事件大資料分析引擎架構概覽

Snuba 是一個在 Clickhouse 基礎上提供豐富資料模型、快速攝取消費者和查詢優化器的服務。以搜尋和提供關于 Sentry 事件資料的聚合引擎。

資料完全存儲在 Clickhouse 表和物化視圖中，它通過輸入流(目前隻有 Kafka 主題)攝入，可以通過時間點查詢或流查詢(訂閱)進行查詢。

文檔：

• https://getsentry.github.io/snuba/architecture/overview.html

Kubernetes ClickHouse Operator

什麼是 Kubernetes Operator？

Kubernetes Operator 是一種封裝、部署和管理 Kubernetes 應用的方法。我們使用 Kubernetes API（應用程式設計接口）和 kubectl 工具在 Kubernetes 上部署并管理 Kubernetes 應用。

• https://kubernetes.io/zh-cn/docs/concepts/extend-kubernetes/operator/

Altinity Operator for ClickHouse

Altinity：ClickHouse Operator 業界領先開源提供商。

• Altinity：https://altinity.com/
• GitHub：https://github.com/Altinity/clickhouse-operator
• Youtube：https://www.youtube.com/@Altinity

當然這種多租戶隔離的 ClickHouse 中間件 PaaS 雲平台，公司或雲廠商幾乎是不開源的。

RadonDB ClickHouse

• https://github.com/radondb/radondb-clickhouse-operator
• https://github.com/radondb/radondb-clickhouse-kubernetes

雲廠商(青雲)基于 altinity-clickhouse-operator 定制的。對于快速部署生産叢集做了些優化。

Helm + Operator 快速上雲 ClickHouse 叢集

雲原生實驗環境

• VKE K8S Cluster，Vultr 托管叢集(v1.23.14)
• Kubesphere v3.3.1 叢集可視化管理，全棧的 Kubernetes 容器雲 PaaS 解決方案。
• Longhorn 1.14，Kubernetes 的雲原生分布式塊存儲。

部署 clickhouse-operator

這裡我們使用 RadonDB 定制的 Operator。

1. values.operator.yaml 定制如下兩個參數：

# operator 監控叢集所有 namespace 的 clickhouse 部署
watchAllNamespaces: true
# 啟用 operator 名額監控
enablePrometheusMonitor: true
           

1. helm 部署 operator：

cd vip-k8s-paas/10-cloud-native-clickhouse

# 部署在 kube-system
helm install clickhouse-operator ./clickhouse-operator -f values.operator.yaml -n kube-system

kubectl -n kube-system get po | grep clickhouse-operator
# clickhouse-operator-6457c6dcdd-szgpd       1/1     Running   0          3m33s

kubectl -n kube-system get svc | grep clickhouse-operator
# clickhouse-operator-metrics   ClusterIP      10.110.129.244   <none>  8888/TCP    4m18s

kubectl api-resources | grep clickhouse
# clickhouseinstallations            chi          clickhouse.radondb.com/v1              true         ClickHouseInstallation
# clickhouseinstallationtemplates    chit         clickhouse.radondb.com/v1              true         ClickHouseInstallationTemplate
# clickhouseoperatorconfigurations   chopconf     clickhouse.radondb.com/v1              true         ClickHouseOperatorConfiguration
           

部署 clickhouse-cluster

這裡我們使用 RadonDB 定制的 clickhouse-cluster helm charts。

快速部署 2 shards + 2 replicas + 3 zk nodes 的叢集。

1. values.cluster.yaml 定制：

clickhouse:
    clusterName: snuba-clickhouse-nodes
    shardscount: 2
    replicascount: 2
...
zookeeper:
  install: true
  replicas: 3
           

1. helm 部署 clickhouse-cluster：

kubectl create ns cloud-clickhouse
helm install clickhouse ./clickhouse-cluster -f values.cluster.yaml -n cloud-clickhouse

kubectl get po -n cloud-clickhouse
# chi-clickhouse-snuba-ck-nodes-0-0-0   3/3     Running   5 (6m13s ago)   16m
# chi-clickhouse-snuba-ck-nodes-0-1-0   3/3     Running   1 (5m33s ago)   6m23s
# chi-clickhouse-snuba-ck-nodes-1-0-0   3/3     Running   1 (4m58s ago)   5m44s
# chi-clickhouse-snuba-ck-nodes-1-1-0   3/3     Running   1 (4m28s ago)   5m10s
# zk-clickhouse-0                       1/1     Running   0               17m
# zk-clickhouse-1                       1/1     Running   0               17m
# zk-clickhouse-2                       1/1     Running   0               17m
           

借助 Operator 快速擴充 clickhouse 分片叢集

1. 使用如下指令，将 shardsCount 改為 3：

kubectl edit chi/clickhouse -n cloud-clickhouse
           

1. 檢視 pods：

kubectl get po -n cloud-clickhouse

# NAME                                  READY   STATUS    RESTARTS       AGE
# chi-clickhouse-snuba-ck-nodes-0-0-0   3/3     Running   5 (24m ago)    34m
# chi-clickhouse-snuba-ck-nodes-0-1-0   3/3     Running   1 (23m ago)    24m
# chi-clickhouse-snuba-ck-nodes-1-0-0   3/3     Running   1 (22m ago)    23m
# chi-clickhouse-snuba-ck-nodes-1-1-0   3/3     Running   1 (22m ago)    23m
# chi-clickhouse-snuba-ck-nodes-2-0-0   3/3     Running   1 (108s ago)   2m33s
# chi-clickhouse-snuba-ck-nodes-2-1-0   3/3     Running   1 (72s ago)    119s
# zk-clickhouse-0                       1/1     Running   0              35m
# zk-clickhouse-1                       1/1     Running   0              35m
# zk-clickhouse-2                       1/1     Running   0              35m
           

發現多出 chi-clickhouse-snuba-ck-nodes-2-0-0 與 chi-clickhouse-snuba-ck-nodes-2-1-0。 分片與副本已自動由 Operator 建立。

小試牛刀

ReplicatedMergeTree+Distributed+Zookeeper 建構多分片多副本叢集

連接配接 clickhouse

我們進入 Pod, 使用原生指令行用戶端 clickhouse-client 連接配接。

kubectl exec -it chi-clickhouse-snuba-ck-nodes-0-0-0 -n cloud-clickhouse -- bash
kubectl exec -it chi-clickhouse-snuba-ck-nodes-0-1-0 -n cloud-clickhouse -- bash
kubectl exec -it chi-clickhouse-snuba-ck-nodes-1-0-0 -n cloud-clickhouse -- bash
kubectl exec -it chi-clickhouse-snuba-ck-nodes-1-1-0 -n cloud-clickhouse -- bash
kubectl exec -it chi-clickhouse-snuba-ck-nodes-2-0-0 -n cloud-clickhouse -- bash
kubectl exec -it chi-clickhouse-snuba-ck-nodes-2-1-0 -n cloud-clickhouse -- bash
           

我們直接通過終端分别進入這 6 個 pod。然後進行測試：

clickhouse-client --multiline -u username -h ip --password passowrd
# clickhouse-client -m
           

建立分布式資料庫

1. 檢視 system.clusters

select * from system.clusters;
           

2.建立名為 test 的資料庫

create database test on cluster 'snuba-ck-nodes';
# 删除：drop database test on cluster 'snuba-ck-nodes';
           

1. 在各個節點檢視，都已存在 test 資料庫。

show databases;
           

建立本地表(ReplicatedMergeTree)

1. 建表語句如下：

在叢集中各個節點 test 資料庫中建立 t_local 本地表，采用 ReplicatedMergeTree 表引擎，接受兩個參數：

• zoo_path — zookeeper 中表的路徑，針對表同一個分片的不同副本，定義相同路徑。'/clickhouse/tables/{shard}/test/t_local'
• replica_name — zookeeper 中表的副本名稱

CREATE TABLE test.t_local on cluster 'snuba-ck-nodes'
(
    EventDate DateTime,
    CounterID UInt32,
    UserID UInt32
)
ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/test/t_local', '{replica}')
PARTITION BY toYYYYMM(EventDate)
ORDER BY (CounterID, EventDate, intHash32(UserID))
SAMPLE BY intHash32(UserID);
           

1. 宏(macros)占位符：

建表語句參數包含的宏替換占位符(如：{replica})。會被替換為配置檔案裡 macros 部分的值。

檢視叢集中 clickhouse 分片&副本節點 configmap：

kubectl get configmap -n cloud-clickhouse | grep clickhouse

NAME                                             DATA   AGE
chi-clickhouse-common-configd                    6      20h
chi-clickhouse-common-usersd                     6      20h
chi-clickhouse-deploy-confd-snuba-ck-nodes-0-0   2      20h
chi-clickhouse-deploy-confd-snuba-ck-nodes-0-1   2      20h
chi-clickhouse-deploy-confd-snuba-ck-nodes-1-0   2      20h
chi-clickhouse-deploy-confd-snuba-ck-nodes-1-1   2      20h
chi-clickhouse-deploy-confd-snuba-ck-nodes-2-0   2      19h
chi-clickhouse-deploy-confd-snuba-ck-nodes-2-1   2      19h
           

檢視節點配置值：

kubectl describe configmap chi-clickhouse-deploy-confd-snuba-ck-nodes-0-0  -n cloud-clickhouse
           

建立對應的分布式表(Distributed)

CREATE TABLE test.t_dist on cluster 'snuba-ck-nodes'
(
    EventDate DateTime,
    CounterID UInt32,
    UserID UInt32
)
ENGINE = Distributed('snuba-ck-nodes', test, t_local, rand());

# drop table test.t_dist on cluster 'snuba-ck-nodes';
           

這裡，Distributed 引擎的所用的四個參數：

• cluster - 服務為配置中的叢集名(snuba-ck-nodes)
• database - 遠端資料庫名(test)
• table - 遠端資料表名(t_local)
• sharding_key - (可選) 分片key(CounterID/rand())

檢視相關表，如：

use test;
show tables;
# t_dist
# t_local
           

通過分布式表插入幾條資料：

# 插入
INSERT INTO test.t_dist VALUES ('2022-12-16 00:00:00', 1, 1),('2023-01-01 00:00:00',2, 2),('2023-02-01 00:00:00',3, 3);
           

任一節點查詢資料：

select * from test.t_dist;
           

實戰，為 Snuba 引擎提供 ClickHouse PaaS

拆解與分析 Sentry Helm Charts

在我們遷移到 Kubernetes Operator 之前，我們先拆解與分析下 sentry-charts 中自帶的 clickhouse & zookeeper charts。

非官方 Sentry Helm Charts：

• https://github.com/sentry-kubernetes/charts

他的 Chart.yaml 如下：

apiVersion: v2
appVersion: 22.11.0
dependencies:
- condition: sourcemaps.enabled
  name: memcached
  repository: https://charts.bitnami.com/bitnami
  version: 6.1.5
- condition: redis.enabled
  name: redis
  repository: https://charts.bitnami.com/bitnami
  version: 16.12.1
- condition: kafka.enabled
  name: kafka
  repository: https://charts.bitnami.com/bitnami
  version: 16.3.2
- condition: clickhouse.enabled
  name: clickhouse
  repository: https://sentry-kubernetes.github.io/charts
  version: 3.2.0
- condition: zookeeper.enabled
  name: zookeeper
  repository: https://charts.bitnami.com/bitnami
  version: 9.0.0
- alias: rabbitmq
  condition: rabbitmq.enabled
  name: rabbitmq
  repository: https://charts.bitnami.com/bitnami
  version: 8.32.2
- condition: postgresql.enabled
  name: postgresql
  repository: https://charts.bitnami.com/bitnami
  version: 10.16.2
- condition: nginx.enabled
  name: nginx
  repository: https://charts.bitnami.com/bitnami
  version: 12.0.4
description: A Helm chart for Kubernetes
maintainers:
- name: sentry-kubernetes
name: sentry
type: application
version: 17.9.0
           

這個 sentry-charts 将所有中間件 helm charts 耦合依賴在一起部署，不适合 sentry 微服務 & 中間件叢集擴充。更進階的做法是每個中間件擁有定制的 Kubernetes Operator(如：clickhouse-operator ) & 獨立的 K8S 叢集，形成中間件 PaaS 平台對外提供服務。

這裡我們拆分中間件 charts 到獨立的 namespace 或單獨的叢集運維。設計為：

• ZooKeeper 命名空間：cloud-zookeeper-paas
• ClickHouse 命名空間：cloud-clickhouse-paas

獨立部署 ZooKeeper Helm Chart

這裡 zookeeper chart 采用的是 bitnami/zookeeper，他的倉庫位址如下：

• https://github.com/bitnami/charts/tree/master/bitnami/zookeeper
• https://github.com/bitnami/containers/tree/main/bitnami/zookeeper
• ZooKeeper Operator 會在後續文章專項讨論。

1. 建立命名空間：

kubectl create ns cloud-zookeeper-paas
           

1. 簡單定制下 values.yaml：

# 暴露下 prometheus 監控所需的服務
metrics:
  containerPort: 9141
  enabled: true
....
....
service:
  annotations: {}
  clusterIP: ""
  disableBaseClientPort: false
  externalTrafficPolicy: Cluster
  extraPorts: []
  headless:
    annotations: {}
    publishNotReadyAddresses: true
  loadBalancerIP: ""
  loadBalancerSourceRanges: []
  nodePorts:
    client: ""
    tls: ""
  ports:
    client: 2181
    election: 3888
    follower: 2888
    tls: 3181
  sessionAffinity: None
  type: ClusterIP
           

注意：在使用支援外部負載均衡器的雲提供商的服務時，需設定 Sevice 的 type 的值為 "LoadBalancer"， 将為 Service 提供負載均衡器。來自外部負載均衡器的流量将直接重定向到後端 Pod 上，不過實際它們是如何工作的，這要依賴于雲提供商。

1. helm 部署：

helm install zookeeper ./zookeeper -f values.yaml -n cloud-zookeeper-paas
           

叢集内，可使用 zookeeper.cloud-zookeeper-paas.svc.cluster.local:2181 對外提供服務。

1. zkCli 連接配接 ZooKeeper：

export POD_NAME=$(kubectl get pods --namespace cloud-zookeeper-paas -l "app.kubernetes.io/name=zookeeper,app.kubernetes.io/instance=zookeeper,app.kubernetes.io/component=zookeeper" -o jsonpath="{.items[0].metadata.name}")

kubectl -n cloud-zookeeper-paas exec -it $POD_NAME -- zkCli.sh

# test
[zk: localhost:2181(CONNECTED) 0] ls /
[zookeeper]
[zk: localhost:2181(CONNECTED) 1] ls /zookeeper
[config, quota]
[zk: localhost:2181(CONNECTED) 2] quit

# 外部通路
# kubectl port-forward --namespace cloud-zookeeper-paas svc/zookeeper 2181: & zkCli.sh 127.0.0.1:2181
           

1. 檢視 zoo.cfg

kubectl -n cloud-zookeeper-paas exec -it $POD_NAME -- cat /opt/bitnami/zookeeper/conf/zoo.cfg
           

# The number of milliseconds of each tick
tickTime=2000
# The number of ticks that the initial
# synchronization phase can take
initLimit=10
# The number of ticks that can pass between
# sending a request and getting an acknowledgement
syncLimit=5
# the directory where the snapshot is stored.
# do not use /tmp for storage, /tmp here is just
# example sakes.
dataDir=/bitnami/zookeeper/data
# the port at which the clients will connect
clientPort=2181
# the maximum number of client connections.
# increase this if you need to handle more clients
maxClientCnxns=60
#
# Be sure to read the maintenance section of the
# administrator guide before turning on autopurge.
#
# https://zookeeper.apache.org/doc/current/zookeeperAdmin.html#sc_maintenance
#
# The number of snapshots to retain in dataDir
autopurge.snapRetainCount=3
# Purge task interval in hours
# Set to "0" to disable auto purge feature
autopurge.purgeInterval=0

## Metrics Providers
#
# https://prometheus.io Metrics Exporter
metricsProvider.className=org.apache.zookeeper.metrics.prometheus.PrometheusMetricsProvider
#metricsProvider.httpHost=0.0.0.0
metricsProvider.httpPort=9141
metricsProvider.exportJvmInfo=true
preAllocSize=65536
snapCount=100000
maxCnxns=0
reconfigEnabled=false
quorumListenOnAllIPs=false
4lw.commands.whitelist=srvr, mntr, ruok
maxSessionTimeout=40000
admin.serverPort=8080
admin.enableServer=true
server.1=zookeeper-0.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local:2888:3888;2181
server.2=zookeeper-1.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local:2888:3888;2181
server.3=zookeeper-2.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local:2888:3888;2181
           

獨立部署 ClickHouse Helm Chart

這裡 clickhouse chart 采用的是 sentry-kubernetes/charts 自己維護的一個版本：

• sentry snuba 目前對于 clickhouse 21.x 等以上版本支援的并不友好，這裡的鏡像版本是 yandex/clickhouse-server:20.8.19.4。
• https://github.com/sentry-kubernetes/charts/tree/develop/clickhouse
• ClickHouse Operator + ClickHouse Keeper 會在後續文章專項讨論。

這個自帶的 clickhouse-charts 存在些問題，Service 部分需簡單修改下允許配置 "type:LoadBalancer" or "type:NodePort"。

注意：在使用支援外部負載均衡器的雲提供商的服務時，需設定 Sevice 的 type 的值為 "LoadBalancer"， 将為 Service 提供負載均衡器。來自外部負載均衡器的流量将直接重定向到後端 Pod 上，不過實際它們是如何工作的，這要依賴于雲提供商。

1. 建立命名空間：

kubectl create ns cloud-clickhouse-paas
           

1. 簡單定制下 values.yaml：

注意上面 zoo.cfg 的 3 個 zookeeper 執行個體的位址：

server.1=zookeeper-0.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local:2888:3888;2181
server.2=zookeeper-1.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local:2888:3888;2181
server.3=zookeeper-2.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local:2888:3888;2181
           

# 修改 zookeeper_servers
clickhouse:
  configmap:
    zookeeper_servers:
      config:
      - hostTemplate: 'zookeeper-0.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local'
        index: clickhouse
        port: "2181"
      - hostTemplate: 'zookeeper-1.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local'
        index: clickhouse
        port: "2181"
      - hostTemplate: 'zookeeper-2.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local'
        index: clickhouse
        port: "2181"
      enabled: true
      operation_timeout_ms: "10000"
      session_timeout_ms: "30000"

# 暴露下 prometheus 監控所需的服務
metrics:
  enabled: true
           

當然這裡也可以不用 Headless Service，因為是同一個叢集的不同 namespace 的内部通路，是以也可簡單填入 ClusterIP 類型 Sevice：

# 修改 zookeeper_servers
clickhouse:
  configmap:
    zookeeper_servers:
      config:
      - hostTemplate: 'zookeeper.cloud-zookeeper-paas.svc.cluster.local'
        index: clickhouse
        port: "2181"
      enabled: true
      operation_timeout_ms: "10000"
      session_timeout_ms: "30000"

# 暴露下 prometheus 監控所需的服務
metrics:
  enabled: true
           

1. helm 部署：

helm install clickhouse ./clickhouse -f values.yaml -n cloud-clickhouse-paas
           

1. 連接配接 clickhouse

kubectl -n cloud-clickhouse-paas exec -it clickhouse-0 -- clickhouse-client --multiline --host="clickhouse-1.clickhouse-headless.cloud-clickhouse-paas"
           

1. 驗證叢集

show databases;
select * from system.clusters;
select * from system.zookeeper where path = '/clickhouse';
           

目前 ClickHouse 叢集的 ConfigMap

kubectl get configmap -n cloud-clickhouse-paas | grep clickhouse

clickhouse-config    1      28h
clickhouse-metrica   1      28h
clickhouse-users     1      28h
           

clickhouse-config(config.xml)

<yandex>
    <path>/var/lib/clickhouse/</path>
    <tmp_path>/var/lib/clickhouse/tmp/</tmp_path>
    <user_files_path>/var/lib/clickhouse/user_files/</user_files_path>
    <format_schema_path>/var/lib/clickhouse/format_schemas/</format_schema_path>

    <include_from>/etc/clickhouse-server/metrica.d/metrica.xml</include_from>

    <users_config>users.xml</users_config>

    <display_name>clickhouse</display_name>
    <listen_host>0.0.0.0</listen_host>
    <http_port>8123</http_port>
    <tcp_port>9000</tcp_port>
    <interserver_http_port>9009</interserver_http_port>
    <max_connections>4096</max_connections>
    <keep_alive_timeout>3</keep_alive_timeout>
    <max_concurrent_queries>100</max_concurrent_queries>
    <uncompressed_cache_size>8589934592</uncompressed_cache_size>
    <mark_cache_size>5368709120</mark_cache_size>
    <timezone>UTC</timezone>
    <umask>022</umask>
    <mlock_executable>false</mlock_executable>
    <remote_servers incl="clickhouse_remote_servers" optional="true" />
    <zookeeper incl="zookeeper-servers" optional="true" />
    <macros incl="macros" optional="true" />
    <builtin_dictionaries_reload_interval>3600</builtin_dictionaries_reload_interval>
    <max_session_timeout>3600</max_session_timeout>
    <default_session_timeout>60</default_session_timeout>
    <disable_internal_dns_cache>1</disable_internal_dns_cache>

    <query_log>
        <database>system</database>
        <table>query_log</table>
        <partition_by>toYYYYMM(event_date)</partition_by>
        <flush_interval_milliseconds>7500</flush_interval_milliseconds>
    </query_log>

    <query_thread_log>
        <database>system</database>
        <table>query_thread_log</table>
        <partition_by>toYYYYMM(event_date)</partition_by>
        <flush_interval_milliseconds>7500</flush_interval_milliseconds>
    </query_thread_log>

    <distributed_ddl>
        <path>/clickhouse/task_queue/ddl</path>
    </distributed_ddl>
    <logger>
        <level>trace</level>
        <log>/var/log/clickhouse-server/clickhouse-server.log</log>
        <errorlog>/var/log/clickhouse-server/clickhouse-server.err.log</errorlog>
        <size>1000M</size>
        <count>10</count>
    </logger>
</yandex>
           

clickhouse-metrica(metrica.xml)

<yandex>
    <zookeeper-servers>
        <node index="clickhouse">
            <host>zookeeper-0.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local</host>
            <port>2181</port>
        </node>
        <node index="clickhouse">
            <host>zookeeper-1.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local</host>
            <port>2181</port>
        </node>
        <node index="clickhouse">
            <host>zookeeper-2.zookeeper-headless.cloud-zookeeper-paas.svc.cluster.local</host>
            <port>2181</port>
        </node>
        <session_timeout_ms>30000</session_timeout_ms>
        <operation_timeout_ms>10000</operation_timeout_ms>
        <root></root>
        <identity></identity>
    </zookeeper-servers>
    <clickhouse_remote_servers>
        <clickhouse>
            <shard>
                <replica>
                    <internal_replication>true</internal_replication>
                    <host>clickhouse-0.clickhouse-headless.cloud-clickhouse-paas.svc.cluster.local</host>
                    <port>9000</port>
                    <user>default</user>
                    <compression>true</compression>
                </replica>
            </shard>
            <shard>
                <replica>
                    <internal_replication>true</internal_replication>
                    <host>clickhouse-1.clickhouse-headless.cloud-clickhouse-paas.svc.cluster.local</host>
                    <port>9000</port>
                    <user>default</user>
                    <compression>true</compression>
                </replica>
            </shard>
            <shard>
                <replica>
                    <internal_replication>true</internal_replication>
                    <host>clickhouse-2.clickhouse-headless.cloud-clickhouse-paas.svc.cluster.local</host>
                    <port>9000</port>
                    <user>default</user>
                    <compression>true</compression>
                </replica>
            </shard>
        </clickhouse>
    </clickhouse_remote_servers>

    <macros>
        <replica from_env="HOSTNAME"></replica>
        <shard from_env="SHARD"></shard>
    </macros>
</yandex>
           

clickhouse-users(users.xml)

<yandex>
</yandex>
           

Sentry Helm Charts 定制

接入 ClickHouse PaaS, 單叢集多節點

我們簡單修改 values.yml

禁用 sentry-charts 中的 clickHouse & zookeeper

clickhouse:
  enabled: false
zookeeper:
  enabled: false    
           

修改externalClickhouse

externalClickhouse:
  database: default
  host: "clickhouse.cloud-clickhouse-paas.svc.cluster.local"
  httpPort: 8123
  password: ""
  singleNode: false
  clusterName: "clickhouse"
  tcpPort: 9000
  username: default
           

注意：

1. 這裡隻是簡單的叢集内部接入 1 個多節點分片叢集，而 Snuba 系統的設計是允許你接入多個 ClickHouse 多節點多分片多副本叢集，将多個 Schema 分散到不同的叢集，進而實作超大規模吞吐。因為是同一個叢集的不同 namespace 的内部通路，是以這裡簡單填入類型為 ClusterIP Sevice 即可。
2. 注意這裡 singleNode 要設定成 false。因為我們是多節點，同時我們需要提供 clusterName：
3. 源碼分析：
4. 這将用于确定：
5. 将運作哪些遷移（僅本地或本地和分布式表）
6. 查詢中的差異 - 例如是否選擇了 _local 或 _dist 表
7. 以及确定來使用不同的 ClickHouse Table Engines 等。
8. 當然，ClickHouse 本身是一個單獨的技術方向，這裡就不展開讨論了。

部署

helm install sentry ./sentry -f values.yaml -n sentry
           

驗證 _local 與 _dist 表以及 system.zookeeper

kubectl -n cloud-clickhouse-paas exec -it clickhouse-0 -- clickhouse-client --multiline --host="clickhouse-1.clickhouse-headless.cloud-clickhouse-paas"

show databases;

show tables;

select * from system.zookeeper where path = '/clickhouse';
           

進階部分 & 超大規模吞吐

接入 ClickHouse 多叢集/多節點/多分片/多副本的中間件 PaaS

獨立部署多套 VKE LoadBlancer+ VKE K8S Cluster + ZooKeeper-Operator + ClickHouse-Operator，分散 Schema 到不同的叢集以及多節點分片。

分析 Snuba 系統設計

檢視測試用例源碼，了解系統設計與高階配置

關于針對 ClickHouse 叢集各個分片、副本之間的讀寫負載均衡、連接配接池等問題。Snuba 在系統設計、代碼層面部分就已經做了充分的考慮以及優化。

關于 ClickHouse Operator 獨立的多個雲原生編排叢集以及 Snuba 系統設計等進階部分會在 VIP 專欄直播課單獨講解。

更多

• 公衆号：黑客下午茶，直播分享通知
• 雲原生中間件 PaaS 實踐：https://k8s-paas.hacker-linner.com