Sentry 后端云原生中间件实践 ClickHouse PaaS ，为 Snuba 事件分析引擎提供动力

目录(脑图)

ClickHouse PaaS 云原生多租户平台(Altinity.Cloud)

官网：https://altinity.cloud

PaaS 架构概览

设计一个拥有云原生编排能力、支持多云环境部署、自动化运维、弹性扩缩容、故障自愈等特性，同时提供租户隔离、权限管理、操作审计等企业级能力的高性能、低成本的分布式中间件服务是真挺难的。

SaaS 模式交付给用户

Sentry Snuba 事件大数据分析引擎架构概览

Snuba 是一个在 Clickhouse 基础上提供丰富数据模型、快速摄取消费者和查询优化器的服务。以搜索和提供关于 Sentry 事件数据的聚合引擎。

数据完全存储在 Clickhouse 表和物化视图中，它通过输入流(目前只有 Kafka 主题)摄入，可以通过时间点查询或流查询(订阅)进行查询。

文档：

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

Kubernetes ClickHouse Operator

什么是 Kubernetes Operator？

Kubernetes Operator 是一种封装、部署和管理 Kubernetes 应用的方法。我们使用 Kubernetes API（应用编程接口）和 kubectl 工具在 Kubernetes 上部署并管理 Kubernetes 应用。

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

Altinity Operator for ClickHouse

Altinity：ClickHouse Operator 业界领先开源提供商。

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

当然这种多租户隔离的 ClickHouse 中间件 PaaS 云平台，公司或云厂商几乎是不开源的。

RadonDB ClickHouse

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

云厂商(青云)基于 altinity-clickhouse-operator 定制的。对于快速部署生产集群做了些优化。

Helm + Operator 快速上云 ClickHouse 集群

云原生实验环境

• VKE K8S Cluster，Vultr 托管集群(v1.23.14)
  
  

• Kubesphere v3.3.1 集群可视化管理，全栈的 Kubernetes 容器云 PaaS 解决方案。
  
  

• Longhorn 1.14，Kubernetes 的云原生分布式块存储。
  
  

部署 clickhouse-operator

这里我们使用 RadonDB 定制的 Operator。

1. values.operator.yaml 定制如下两个参数：

# operator 监控集群所有 namespace 的 clickhouse 部署
watchAllNamespaces: true
# 启用 operator 指标监控
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

这里我们使用 RadonDB 定制的 clickhouse-cluster helm charts。

快速部署 2 shards + 2 replicas + 3 zk nodes 的集群。

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 快速扩展 clickhouse 分片集群

1. 使用如下命令，将 shardsCount 改为 3：

kubectl edit chi/clickhouse -n cloud-clickhouse

1. 查看 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

发现多出 chi-clickhouse-snuba-ck-nodes-2-0-0 与 chi-clickhouse-snuba-ck-nodes-2-1-0。 分片与副本已自动由 Operator 新建。

小试牛刀

ReplicatedMergeTree+Distributed+Zookeeper 构建多分片多副本集群

连接 clickhouse

我们进入 Pod, 使用原生命令行客户端 clickhouse-client 连接。

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

我们直接通过终端分别进入这 6 个 pod。然后进行测试：

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

创建分布式数据库

1. 查看 system.clusters

select * from system.clusters;

2.创建名为 test 的数据库

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

1. 在各个节点查看，都已存在 test 数据库。

show databases;

创建本地表(ReplicatedMergeTree)

1. 建表语句如下：

在集群中各个节点 test 数据库中创建 t_local 本地表，采用 ReplicatedMergeTree 表引擎，接受两个参数：

• zoo_path — zookeeper 中表的路径，针对表同一个分片的不同副本，定义相同路径。
  • '/clickhouse/tables/{shard}/test/t_local'
• replica_name — zookeeper 中表的副本名称

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：

建表语句参数包含的宏替换占位符(如：{replica})。会被替换为配置文件里 macros 部分的值。

查看集群中 clickhouse 分片&副本节点 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

查看节点配置值：

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

创建对应的分布式表(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';

这里，Distributed 引擎的所用的四个参数：

• cluster - 服务为配置中的集群名(snuba-ck-nodes)
• database - 远程数据库名(test)
• table - 远程数据表名(t_local)
• sharding_key - (可选) 分片key(CounterID/rand())

查看相关表，如：

use test;
show tables;
# t_dist
# t_local

通过分布式表插入几条数据：

# 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);

任一节点查询数据：

select * from test.t_dist;

实战，为 Snuba 引擎提供 ClickHouse PaaS

拆解与分析 Sentry Helm Charts

在我们迁移到 Kubernetes Operator 之前，我们先拆解与分析下 sentry-charts 中自带的 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

这个 sentry-charts 将所有中间件 helm charts 耦合依赖在一起部署，不适合 sentry 微服务 & 中间件集群扩展。更高级的做法是每个中间件拥有定制的 Kubernetes Operator(如：clickhouse-operator ) & 独立的 K8S 集群，形成中间件 PaaS 平台对外提供服务。

这里我们拆分中间件 charts 到独立的 namespace 或单独的集群运维。设计为：

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

独立部署 ZooKeeper Helm Chart

这里 zookeeper chart 采用的是 bitnami/zookeeper，他的仓库地址如下：

• https://github.com/bitnami/charts/tree/master/bitnami/zookeeper
• https://github.com/bitnami/containers/tree/main/bitnami/zookeeper
• ZooKeeper Operator 会在后续文章专项讨论。

1. 创建命名空间：

kubectl create ns cloud-zookeeper-paas

1. 简单定制下 values.yaml：

# 暴露下 prometheus 监控所需的服务
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：在使用支持外部负载均衡器的云提供商的服务时，需设置 Sevice 的 type 的值为 "LoadBalancer"， 将为 Service 提供负载均衡器。来自外部负载均衡器的流量将直接重定向到后端 Pod 上，不过实际它们是如何工作的，这要依赖于云提供商。

1. helm 部署：

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

集群内，可使用 zookeeper.cloud-zookeeper-paas.svc.cluster.local:2181 对外提供服务。

1. zkCli 连接 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

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

1. 查看 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

独立部署 ClickHouse Helm Chart

这里 clickhouse chart 采用的是 sentry-kubernetes/charts 自己维护的一个版本：

• sentry snuba 目前对于 clickhouse 21.x 等以上版本支持的并不友好，这里的镜像版本是 yandex/clickhouse-server:20.8.19.4。
• https://github.com/sentry-kubernetes/charts/tree/develop/clickhouse
• ClickHouse Operator + ClickHouse Keeper 会在后续文章专项讨论。

这个自带的 clickhouse-charts 存在些问题，Service 部分需简单修改下允许配置 "type:LoadBalancer" or "type:NodePort"。

注意：在使用支持外部负载均衡器的云提供商的服务时，需设置 Sevice 的 type 的值为 "LoadBalancer"， 将为 Service 提供负载均衡器。来自外部负载均衡器的流量将直接重定向到后端 Pod 上，不过实际它们是如何工作的，这要依赖于云提供商。

1. 创建命名空间：

kubectl create ns cloud-clickhouse-paas

1. 简单定制下 values.yaml：

注意上面 zoo.cfg 的 3 个 zookeeper 实例的地址：

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 监控所需的服务
metrics:
  enabled: true

当然这里也可以不用 Headless Service，因为是同一个集群的不同 namespace 的内部访问，所以也可简单填入 ClusterIP 类型 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 监控所需的服务
metrics:
  enabled: true

1. helm 部署：

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

1. 连接 clickhouse

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

1. 验证集群

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

当前 ClickHouse 集群的 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, 单集群多节点

我们简单修改 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. 这里只是简单的集群内部接入 1 个多节点分片集群，而 Snuba 系统的设计是允许你接入多个 ClickHouse 多节点多分片多副本集群，将多个 Schema 分散到不同的集群，从而实现超大规模吞吐。因为是同一个集群的不同 namespace 的内部访问，所以这里简单填入类型为 ClusterIP Sevice 即可。

2. 注意这里 singleNode 要设置成 false。因为我们是多节点，同时我们需要提供 clusterName：

   源码分析：
   
   

   这将用于确定：

   • 将运行哪些迁移（仅本地或本地和分布式表）
   • 查询中的差异 - 例如是否选择了 _local 或 _dist 表

   以及确定来使用不同的 ClickHouse Table Engines 等。
   
   

   当然，ClickHouse 本身是一个单独的技术方向，这里就不展开讨论了。

部署

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

验证 _local 与 _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';

高级部分 & 超大规模吞吐

接入 ClickHouse 多集群/多节点/多分片/多副本的中间件 PaaS

独立部署多套 VKE LoadBlancer+ VKE K8S Cluster + ZooKeeper-Operator + ClickHouse-Operator，分散 Schema 到不同的集群以及多节点分片。

分析 Snuba 系统设计

查看测试用例源码，了解系统设计与高阶配置

关于针对 ClickHouse 集群各个分片、副本之间的读写负载均衡、连接池等问题。Snuba 在系统设计、代码层面部分就已经做了充分的考虑以及优化。

关于 ClickHouse Operator 独立的多个云原生编排集群以及 Snuba 系统设计等高级部分会在 VIP 专栏直播课单独讲解。

更多

• 公众号：黑客下午茶，直播分享通知
• 云原生中间件 PaaS 实践：https://k8s-paas.hacker-linner.com