MongoDB Data Skew
MongoDB data skew refers to the uneven distribution of data across the MongoDB nodes or MongoDB shards in a sharded cluster, which can severely impact MongoDB database performance. Data skew occurs when a disproportionate amount of data is stored on a single shard, leading to an imbalance that can cause one or more shards to handle significantly more MongoDB workload than others. This situation is typically a result of poorly chosen shard keys or uneven data characteristics. For example, if a shard key is chosen that doesn't distribute the data evenly across the available shards, it can cause certain nodes to store most of the data, leading to MongoDB hotspots where one shard becomes overloaded with MongoDB read operations and MongoDB write operations, slowing down the system overall.
https://en.wikipedia.org/wiki/MongoDB
Data skew is particularly problematic in MongoDB when it comes to MongoDB performance bottlenecks. If one shard stores most of the data, all MongoDB queries and MongoDB updates that target this data will be funneled to the same shard, creating an uneven workload distribution. This results in increased MongoDB latency and can even cause MongoDB node failures if the overloaded shard becomes unresponsive. MongoDB monitoring and managing data skew is essential in MongoDB sharded clusters, especially when working with MongoDB large datasets that are growing rapidly. MongoDB administrators must ensure that the chosen shard key leads to an even data distribution, as this is one of the most effective ways to avoid data skew and its database performance implications.
https://en.wikipedia.org/wiki/MongoDB
There are several strategies for mitigating data skew in MongoDB. One approach is to carefully select a shard key that evenly distributes data across the shards, ensuring that each shard stores a similar volume of data. Another strategy is to use range-based or hashed shard keys, which can help reduce the likelihood of data skew. MongoDB also provides tools like the MongoDB Atlas monitoring platform, which allows users to track the distribution of data across shards and identify if data skew is occurring. By understanding the patterns of data access and optimizing the data distribution, MongoDB users can minimize the impact of data skew and maintain high performance across their sharded clusters.