Aggregation optimization and performance tuning

Aggregation Optimization and Performance Tuning

MongoDB's aggregation framework provides powerful data processing capabilities, but complex aggregation operations can lead to performance issues. With reasonable optimization strategies, query efficiency can be significantly improved, and resource consumption can be reduced.

Index Optimization

Proper use of indexes is the foundation for improving aggregation performance. Creating appropriate indexes for stages like $match, $sort, and $group in the aggregation pipeline can greatly reduce the number of documents scanned.

// Create a compound index for common aggregation queries
db.orders.createIndex({ status: 1, orderDate: -1 })

// Utilize indexes when executing aggregation queries
db.orders.aggregate([
  { $match: { status: "completed", orderDate: { $gte: ISODate("2023-01-01") } } },
  { $group: { _id: "$customerId", total: { $sum: "$amount" } } }
])

For aggregation pipelines with multiple stages, consider creating covering indexes. When an index includes all query fields, MongoDB can retrieve data directly from the index without accessing the documents themselves.

Pipeline Stage Optimization

The execution order of the aggregation pipeline directly impacts performance. Use $match and $project early to reduce the amount of data processed.

// Unoptimized pipeline
db.sales.aggregate([
  { $project: { item: 1, price: 1 } },
  { $unwind: "$items" },
  { $match: { "items.price": { $gt: 100 } } }
])

// Optimized pipeline
db.sales.aggregate([
  { $match: { "items.price": { $gt: 100 } } },  // Filter first
  { $unwind: "$items" },  // Unwind afterward
  { $project: { item: 1, price: 1 } }  // Project last
])

Avoid operations in the pipeline that may hit memory limits, such as $unwind on large arrays or large $group operations. Use $limit and $skip to process data in stages.

Memory Management

The MongoDB aggregation pipeline has a default memory limit of 100MB. For large datasets, enable the disk usage option:

db.orders.aggregate([
  { $match: { status: "processing" } },
  { $group: { _id: "$productId", count: { $sum: 1 } } }
], { allowDiskUse: true })

For particularly large aggregation operations, consider using $facet for staged processing or replacing complex aggregation pipelines with Map-Reduce.

Query Plan Analysis

Use explain() to analyze the execution plan of aggregation queries:

db.orders.aggregate([
  { $match: { status: "shipped" } },
  { $group: { _id: "$region", total: { $sum: "$amount" } } }
]).explain("executionStats")

Focus on the totalDocsExamined and executionTimeMillis metrics in executionStats to identify performance bottlenecks.

Sharded Cluster Optimization

In a sharded cluster environment, aggregation queries require special consideration:

1. Ensure the $match stage includes the shard key to avoid full shard scans.
2. For operations requiring data merging, consider using the $merge stage instead of $out.
3. Avoid large cross-shard data transfers in the $lookup stage.

// Aggregation optimized with shard key
db.orders.aggregate([
  { $match: { shardKey: "east", orderDate: { $gte: ISODate("2023-01-01") } } },
  { $lookup: {
      from: "customers",
      localField: "customerId",
      foreignField: "_id",
      as: "customer"
  } }
])

Caching Strategies

For frequently executed aggregation queries, consider the following caching strategies:

1. Store results in a collection with a TTL index for automatic expiration.
2. Use the $merge stage to incrementally update aggregation results.
3. Precompute aggregation results for historical data.

// Use $merge to cache aggregation results
db.sales.aggregate([
  { $match: { date: { $gte: ISODate("2023-01-01") } } },
  { $group: { _id: "$product", totalSales: { $sum: "$amount" } } },
  { $merge: { into: "monthly_sales", whenMatched: "replace" } }
])

Monitoring and Tuning

Continuously monitor aggregation query performance:

1. Use MongoDB Profiler to log slow queries.
2. Set appropriate slow query thresholds.
3. Regularly review aggregation query execution plans.

// Enable slow query logging
db.setProfilingLevel(1, { slowms: 100 })

// View slow aggregation queries
db.system.profile.find({ op: "command", "command.aggregate": { $exists: true } })

Advanced Optimization Techniques

For specific scenarios, more advanced optimization techniques can be employed:

1. Use $expr and $function to implement complex logic.
2. Leverage $indexStats to analyze index usage.
3. Apply the bucketing pattern for time-series data.

// Time-series data bucketing example
db.weather.aggregate([
  { $match: { timestamp: { $gte: ISODate("2023-01-01") } } },
  { $bucketAuto: {
      groupBy: "$timestamp",
      buckets: 24,
      output: {
        avgTemp: { $avg: "$temperature" },
        maxTemp: { $max: "$temperature" }
      }
  } }
])

Performance Benchmarking

Establish performance benchmarks to compare the effects of different optimization approaches:

1. Use benchRun for batch query testing.
2. Record execution times under different data volumes.
3. Test the impact of different index configurations.

// Simple benchmarking script
const testAggregation = function() {
  const start = new Date()
  db.orders.aggregate([...])
  return new Date() - start
}

const times = []
for (let i = 0; i < 10; i++) {
  times.push(testAggregation())
}
print(`Average execution time: ${times.reduce((a,b) => a+b, 0)/times.length}ms`)