Sentry 后端云原生中间件实践 ClickHouse PaaS
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。
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
- 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。
values.cluster.yaml定制：
clickhouse:
clusterName: snuba-clickhouse-nodes
shardscount: 2
replicascount: 2
...
zookeeper:
install: true
replicas: 3
- 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
- 使用如下命令，将
shardsCount改为3：
kubectl edit chi/clickhouse -n cloud-clickhouse
- 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
- 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';
- 在各个节点查看，都已存在
test数据库。
show databases;
创建本地表(ReplicatedMergeTree)
- 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);
- 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。
- 创建命名空间：
kubectl create ns cloud-zookeeper-paas
- 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。
- 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。
- 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
- 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。
- 创建命名空间：
kubectl create ns cloud-clickhouse-paas
- 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
- helm 部署：
helm install clickhouse ./clickhouse -f values.yaml -n cloud-clickhouse-paas
- 连接 clickhouse
kubectl -n cloud-clickhouse-paas exec -it clickhouse-0 -- clickhouse-client --multiline --host="clickhouse-1.clickhouse-headless.cloud-clickhouse-paas"
- 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：
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。
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';
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 系统设计等部分会在 VIP 专栏直播课单独讲解。
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