# Finisky Garden

Easy doesn't enter into grown-up life.

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Shard Data Size
mongo-0 10.55 GB
mongo-1 25.76 GB
mongo-2 10.04 GB

mongo-1这个分片的数据大小显著高于其他分片，而三个分片的chunk数目是基本一致的，所以需要分析不同分片上的chunk大小分布。

Recently we found the traffic is not balanced across the MongoDB cluster shards. After investigation, the root cause is that data on each shard is not evenly distributed (Chunk balancing != data balancing != traffic balancing). The data distribution looks like this:

Shard Data Size
mongo-0 10.55 GB
mongo-1 25.76 GB
mongo-2 10.04 GB

Why the data size of mongo-1 is significantly large than others while the chunk number among 3 shards is almost the same? Then we need to analysis the chunk size distribution across these shards.

After adding new shards to our production MongoDB cluster (v4.4.6-ent with 5 shards, 3 replicas for each shard), we found that the balancer is not working. sh.status() displays many chunk migration errors:

...
balancer:
Currently enabled:  yes
Currently running:  no
Failed balancer rounds in last 5 attempts:  0
Migration Results for the last 24 hours:
7 : Failed with error 'aborted', from mongo-1 to mongo-3
7208 : Failed with error 'aborted', from mongo-1 to mongo-4
databases:
{  "_id" : "X",  "primary" : "mongo-1",  "partitioned" : true,  "version" : {  "uuid" : UUID("xxx"),  "lastMod" : 1 } }
X.A
shard key: { "Uuid" : 1 }
unique: false
balancing: true
chunks:
mongo-0       231
mongo-1       327
mongo-2       230
mongo-3       208
...

When a pod in error state (crashloopbackoff), kubernetes would restart the pod. If you try to exec into the pod to check the log or debug, the following error message appears:

unable to upgrade connection: container not found ("")

Because the old pod has been killed and you cannot exec into it anymore. So how can we prevent the pod from endless restart?

Multiple Backup Daemons are typically run when the storage requirements or the load generated by the deployment is too much for a single daemon.

Directly scale the statefulset ops-manager-backup-daemon to multiple instances (e.g. 3) doesn't work. Because the mongodb-enterprise-operator is watching the statefulset, the instance number will be scaled down to 1 by the MongoDB operator several miniutes later.

So how to scale up the backup dameons by the MongoDB kubernetes operator?

# Apex Compile Error

The environment (cuda10.0):

\$ conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch

commit 0c2c6eea6556b208d1a8711197efc94899e754e1 (HEAD -> master, origin/master, origin/HEAD)
Date:   Sat Jul 17 08:53:59 2021 +0800
...

Change stream是什么？官方文档:

Change streams allow applications to access real-time data changes without the complexity and risk of tailing the oplog. Applications can use change streams to subscribe to all data changes on a single collection, a database, or an entire deployment, and immediately react to them. Because change streams use the aggregation framework, applications can also filter for specific changes or transform the notifications at will.

• TLS验证有些问题，通过修改源码解决了
• all同步模式下，只能通过oplog进行了全量复制，在用change stream进行增量复制时不停抛错，无法正常运行

By the official manual:

Change streams allow applications to access real-time data changes without the complexity and risk of tailing the oplog. Applications can use change streams to subscribe to all data changes on a single collection, a database, or an entire deployment, and immediately react to them. Because change streams use the aggregation framework, applications can also filter for specific changes or transform the notifications at will.

Here we leverage change stream to replicate data from one MongoDB to another in realtime.

There is some existing tools such as MongoShake do the same thing. However, MongoShake is a little bit complicated to use. We encoutered two issues:

• Modify the source code to use TLS authentication
• Cannot perform increment sync in all sync_mode

Since our goal is realtime replication, we choose a more straightforward and controllable way: MongoDB Ops Manager cluster restore and change stream to apply realtime changes.

MongoDB change stream is a nice feature. It allows applications to access real-time data changes without the complexity and risk of tailing the oplog.

Recently, when we use change stream to replicate data from one sharded cluster to another, it immediately made the cluster unstable (broke down several nodes and triggered the primary change). Then the read/write operations latency significantly increased.