MongoDB Academy · 课时

运行跨数据源聚合管道

您将编写聚合管道，在单个查询中连接 Atlas 集合数据与存储在 S3 中的 JSON 或 Parquet 文件。

运行跨数据源聚合管道 是 CoddyKit 上的免费 MongoDB Academy 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 MongoDB Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 MongoDB Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

What Makes Cross-Source Pipelines Special?

A cross-source aggregation pipeline in Atlas Data Federation runs the same aggregation stages you know from MongoDB, but the data under each stage may come from different physical systems — an S3 bucket, a live Atlas cluster, or both. The federated query engine handles all the routing, fan-out, and result merging transparently. From your application's perspective, it looks like a single MongoDB collection query.

Simple Cross-Source Find

The simplest cross-source query is a find() on a virtual collection backed by S3 files. The query engine reads the files, parses them, and applies the filter. Fields in the filter that match partition attributes in the path cause automatic file pruning. Fields that do not match partition attributes are applied as a post-read filter.

// Virtual collection 'events' backed by S3 JSON files
// Path: /data/events/{year int}/{month int}/*.json

// This query prunes to /data/events/2025/1/ only
const jan2025 = await db.collection('events').find({
  year: 2025,
  month: 1,
  eventType: 'purchase'   // post-read filter (not a partition attr)
}).toArray()

Aggregating S3 Data Like a Live Collection

You can run any aggregation stage against S3-backed virtual collections: $match, $group, $project, $sort, $limit. The query engine pushes down stages where possible (especially $match for partition pruning and column pruning in Parquet) and executes remaining stages in its own compute layer after reading the data.

// Group S3-archived events by region and count
const summary = await db.collection('events_2024').aggregate([
  { $match: { year: 2024, month: { $in: [10, 11, 12] } } },  // pruning
  { $group: { _id: '$region', total: { $sum: 1 }, revenue: { $sum: '$amount' } } },
  { $sort: { revenue: -1 } },
  { $limit: 10 }
]).toArray()

Joining Atlas and S3 With $lookup

The most powerful cross-source pattern is using $lookup to join a live Atlas collection with an S3-archived collection. Start the pipeline from the live collection (the 'driver') and look up into the virtual S3-backed collection. Always place a $match early to minimize the number of lookups performed.

// Join live customers (Atlas) with archived orders (S3)
const result = await db.collection('customers').aggregate([
  { $match: { tier: 'gold', region: 'EU' } },   // filter live data first
  { $lookup: {
    from: 'orders_archive',   // virtual S3-backed collection
    let: { custId: '$_id' },
    pipeline: [
      { $match: { $expr: { $eq: ['$customerId', '$$custId'] } } },
      { $project: { orderId: 1, amount: 1, date: 1 } }
    ],
    as: 'orderHistory'
  }},
  { $addFields: { totalSpend: { $sum: '$orderHistory.amount' } } },
  { $sort: { totalSpend: -1 } },
  { $limit: 50 }
]).toArray()

Aggregating Across Multiple Atlas Clusters

If your federated instance has multiple Atlas cluster stores, you can join collections from different Atlas clusters in a single pipeline. This is useful for multi-tenant or multi-region deployments where data is sharded across separate clusters and you need cross-cluster reports without merging clusters or building a separate reporting database.

// Virtual collections pointing to different Atlas clusters
// 'orders_us' -> Atlas cluster in US
// 'orders_eu' -> Atlas cluster in EU

// Union results from two clusters
db.orders_us.aggregate([
  { $match: { date: { $gte: ISODate('2025-01-01') } } },
  { $unionWith: {
    coll: 'orders_eu',
    pipeline: [{ $match: { date: { $gte: ISODate('2025-01-01') } } }]
  }},
  { $group: { _id: '$status', count: { $sum: 1 } } }
])

Writing Results to Atlas With $out / $merge

After running a cross-source aggregation, you can write the results back to a live Atlas collection using $out or $merge. This is the ETL pattern: read historical data from S3, join with live data, compute aggregates, and write the results to a materialised view collection in Atlas that application queries can then read cheaply and quickly.

// ETL: aggregate S3 archive + Atlas, write result to Atlas
db.events_2024.aggregate([
  { $match: { year: 2024 } },
  { $group: {
    _id: { region: '$region', month: '$month' },
    sessions: { $sum: 1 },
    revenue: { $sum: '$amount' }
  }},
  { $merge: {
    into: { db: 'reporting', coll: 'monthly_summary' },
    whenMatched: 'replace',
    whenNotMatched: 'insert'
  }}
])

Parquet Column Pruning: Only Read What You Need

When querying Parquet files, Data Federation applies column pruning: if your $project stage specifies only certain fields, the query engine reads only those columns from the Parquet file (which stores data column-by-column). This can reduce bytes read by 90%+ compared to reading every column. Place your $project as early as possible in the pipeline for maximum column pruning benefit.

// Column pruning: only reads 'region', 'amount', 'date' columns from Parquet
db.events_2024.aggregate([
  { $project: { region: 1, amount: 1, date: 1, _id: 0 } },  // early project
  { $match: { region: 'EU' } },
  { $group: { _id: '$region', totalRevenue: { $sum: '$amount' } } }
])
// Other columns (userId, sessionId, metadata, etc.) are never read from disk

Federated Query Performance Monitoring

Atlas Data Federation logs query execution details in the Atlas UI under the Query History tab. Each query shows: bytes processed, execution time, and the number of files/partitions scanned. High bytes-processed numbers usually mean partition attributes are missing or the query does not match any partition keys. Use this log to tune your storage configuration and query patterns.

// Get query stats via the admin DB on the federated instance
db.adminCommand({ currentOp: 1 })
// Shows active federated queries with bytes read, duration

// In Atlas UI: Data Federation > Query History
// Shows past queries, duration, data processed, and cost estimate

Handling Schema Differences Across Sources

S3 files from different time periods or systems may have different schemas (field names, types, structure). Data Federation handles this gracefully — missing fields return null, extra fields are included. You can use $ifNull, $cond, and $convert in your pipeline to normalise varying schemas before grouping or joining.

// Normalise schema variations across old and new S3 file formats
db.events.aggregate([
  { $addFields: {
    // Old format: 'user_id', New format: 'userId'
    userId: { $ifNull: ['$userId', '$user_id'] },
    // Old format: string amount, New format: number
    amount: { $convert: { input: '$amount', to: 'double', onError: 0 } }
  }},
  { $group: { _id: '$userId', total: { $sum: '$amount' } } }
])

Caching Federated Query Results

Data Federation does not cache results between queries — each query re-reads the underlying sources. For dashboards that run the same report repeatedly, use the ETL pattern: schedule an aggregation that writes results to an Atlas collection via $merge, then have your dashboard query the fast Atlas collection. Atlas Triggers can schedule this refresh on any cron interval.

Limitations of Cross-Source Pipelines

Be aware of current limitations: 1) Transactions are not supported on federated instances. 2) Index usage only applies to Atlas-backed collections, not S3 files. 3) Very large result sets may time out — use $out/$merge to write results instead of streaming them back. 4) Latency is higher than a live Atlas query due to S3 I/O — not suitable for user-facing, real-time queries.

Quick Check

Test your understanding of MongoDB & NoSQL Databases concepts from this lesson.

Lesson Recap

In this lesson you learned: cross-source aggregation pipelines use the same MongoDB stages against virtual collections backed by S3 or Atlas clusters, $lookup enables joining live Atlas data with S3 archives in a single pipeline, and early $project enables column pruning in Parquet files to dramatically reduce bytes scanned. Next up we explore S3 data partitioning for query performance.

常见问题解答

「运行跨数据源聚合管道」课时是免费的吗？

是的 — 「运行跨数据源聚合管道」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 MongoDB Academy 课程的其余内容，请升级到 CoddyKit PRO。 MongoDB Academy 课程共包含 4 节课。

「运行跨数据源聚合管道」这节课中我会学到什么？

您将编写聚合管道，在单个查询中连接 Atlas 集合数据与存储在 S3 中的 JSON 或 Parquet 文件。你通过在浏览器中直接运行的动手代码来练习 MongoDB Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 MongoDB Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 MongoDB Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「运行跨数据源聚合管道」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 MongoDB Academy 课中编写并运行代码吗？

能。每节 MongoDB Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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