Monitoring and optimization of sharded clusters

Overview of Sharded Cluster Architecture

MongoDB sharded clusters achieve horizontal scaling by distributing data across multiple shards, primarily consisting of three core components: the mongos routing process, config servers, and shard replica sets. The mongos acts as a query router, receiving client requests and routing operations to the appropriate shards. Config servers store the cluster's metadata and shard key range information. Each shard is typically a replica set responsible for storing actual data chunks. This architectural design allows the system to overcome single-machine storage and performance limitations but also introduces complexities in monitoring and optimization.

// Example code for connecting to a sharded cluster
const { MongoClient } = require('mongodb');
const uri = "mongodb://mongos1:27017,mongos2:27017/?replicaSet=mongosRS";
const client = new MongoClient(uri);

async function connectToShardedCluster() {
  try {
    await client.connect();
    console.log("Connected to sharded cluster via mongos");
  } catch (e) {
    console.error("Connection failed", e);
  }
}

Key Monitoring Metrics

Shard Balancing State Monitoring

The sh.status() command can be used to view the overall state of the sharded cluster, with a focus on whether data distribution is balanced. Uneven shard distribution can lead to hotspot issues, where certain shards are overloaded while others are underutilized. Use db.collection.getShardDistribution() to view the data distribution of a specific collection across shards.

// Example shell commands for checking shard status
db.adminCommand({ listShards: 1 });
db.collection.getShardDistribution();

Performance Metrics Monitoring

Chunk migration operations require special attention, as frequent migrations can impact cluster performance. Monitor the connection pool status and routing table cache hit rate of mongos, as well as core metrics such as operation latency and queue length for each shard. Use $currentOp to view real-time operations.

// Example for monitoring current operations
db.adminCommand({
  currentOp: true,
  $or: [
    { op: { $ne: "none" } },
    { "command.shardCollection": { $exists: true } }
  ]
});

Query Routing Optimization

Shard Key Selection Strategy

The choice of shard key directly affects query performance. An ideal shard key should have high cardinality, low frequency of changes, and match query patterns. Avoid using monotonically increasing shard keys, as this can cause all new data to be written to a single shard. Compound shard keys can better distribute write loads.

// Example of creating a compound shard key
sh.shardCollection("orders.records", {
  "customerId": 1,
  "orderDate": -1
});

Targeted Query Optimization

Using the shard key for targeted queries can significantly improve performance by avoiding broadcast queries across all shards. When query conditions include the complete shard key, mongos can accurately route to a specific shard. For queries without the shard key, consider creating appropriate indexes to optimize performance.

// Example of a targeted query using the shard key
db.orders.find({
  "customerId": "C12345",
  "orderDate": { $gte: ISODate("2023-01-01") }
}).explain("executionStats");

Data Balancing Strategy Adjustments

Automatic Balancer Configuration

MongoDB's automatic balancer is responsible for migrating chunks between shards to maintain data balance. Configure the balancer window to perform balancing operations during off-peak business hours. Use sh.setBalancerState() to control the balancer state and sh.getBalancerState() to check the current state.

// Example of configuring the balancer window
use config
db.settings.update(
  { _id: "balancer" },
  { $set: { activeWindow: {
    start: "23:00",
    stop: "04:00"
  }}},
  { upsert: true }
);

Manual Chunk Management

For oversized chunks, manually use the splitChunk command to split them. In special cases, the moveChunk command may be needed to manually migrate data. These operations should be performed during maintenance windows, with close monitoring of their impact on cluster performance.

// Example of manually splitting a chunk
db.adminCommand({
  splitChunk: "orders.records",
  find: { customerId: "C20000" },
  bounds: [
    { customerId: MinKey, orderDate: MinKey },
    { customerId: MaxKey, orderDate: MaxKey }
  ]
});

Index Optimization Strategies

Global vs. Local Indexes

Sharded collections support two types of indexes: global indexes (created identically on all shards) and local indexes (created only on specific shards). Choosing the correct index type requires considering query patterns and performance needs. Global indexes support more efficient cross-shard queries but increase write overhead.

// Example of creating a global index
db.orders.createIndex({
  "productId": 1,
  "status": 1
}, { name: "global_product_status" });

Index Maintenance Monitoring

Regularly check index usage and remove unused redundant indexes. Use $indexStats to collect index usage statistics, focusing on index selectivity and hit rates. In a sharded environment, index maintenance requires coordination across multiple shards.

// Example of checking index usage
db.orders.aggregate([{ $indexStats: {} }]);

Connection Management and Configuration

mongos Connection Pool Optimization

Configure the mongos connection pool size appropriately to balance resource usage and performance needs. Monitor connection pool statistics, including active and waiting connections. Multiple application servers should use local mongos instances to avoid single-point bottlenecks.

// Example of Node.js driver connection pool configuration
const client = new MongoClient(uri, {
  poolSize: 50,
  minPoolSize: 10,
  maxIdleTimeMS: 30000,
  waitQueueTimeoutMS: 5000
});

Read/Write Concern Configuration

Set read/write concern levels according to application requirements. In a sharded environment, higher read/write concern levels increase latency but ensure stronger consistency. Use readPreference to control query routing strategies and optimize read performance.

// Example of setting read/write concern
db.orders.find({ status: "pending" })
  .readPref("secondary")
  .readConcern("majority")
  .writeConcern({
    w: "majority",
    j: true,
    wtimeout: 5000
  });

Troubleshooting and Handling

Performance Bottleneck Analysis

Use explain() to analyze query execution plans and identify slow queries. Focus on the SHARDING_FILTER and SHARD_MERGE stages, which may indicate queries accessing multiple shards. Use tools like mongotop and mongostat to monitor real-time resource usage.

// Example of analyzing query execution plans
db.orders.find({ customerId: "C12345" })
  .explain("executionStats");

Common Issue Handling

Address scenarios such as config server unavailability or shard replica set failures. When a shard is unavailable, understand that some queries may still succeed (depending on read/write concern settings). Establish a robust monitoring and alerting system to detect and resolve cluster anomalies promptly.

// Example commands for checking replica set status
rs.status();
db.serverStatus({ repl: 1 });