I recently shared two posts (here and here) with results for sysbench on a large server using Postgres versions 10 through 17. In general there were several large improvements over time, but one small regression that arrived in Postgres 11.x. This blog post provides more details on the problem using results from Postgres 10.23, 11.22 and 17.4.

The regression occurs starting in Postgres 11.22 because Postgres is more likely to use bitmap index scan starting in 11.x. I have yet to learn why or how to prevent that.

Index scan vs bitmap index scan

Experts gave me great advice based on a few flamegraphs that I shared. It looked like Postgres started to use bitmap index scan more often starting in Postgres 11. Upstream sysbench does collect query plans for the SQL that it uses, so I modified my fork to do that.

While the explain output helps, it would help even more were there a feature in Postgres to provide optimizer traces, similar to what MySQL has, to understand why some query plans are selected and others are rejected. Another feature request is to improve the official Postgres docs to provide more detail on 1) the difference between index scan and bitmap index scan and 2) the difference between lossy and non-lossy bitmap index scans (AFAIK, one needs recheck).

The problem microbenchmark

The microbenchmarks in question do a range scan with aggregation and have a name like read-only_range=$X where X has been 10, 100 and 1000 but here I use X in 10, 100, 1000, 2000, 4000, 8000, 16000 and 32000. The value of X is the length of the range scan. These are run via oltp_read_only.lua and use these SQL statements.

Build, configuration, hardware, benchmark

These are described in my previous post. But a few things have changed for this report

• I only tested Postgres versions 10.23, 11.22 and 17.4
• I ran the read-only_range=$X microbenchmark for X in 10, 100, 1000, 2000, 4000, 8000, 16000 and 32000. Previously I ran it for X in 10, 100, 10000
• I added new tests that each run only one of the SQL statements used by oltp_read_only.lua. All of the Lua scripts are here.

There are a few interesting scenarios to keep in mind when writing applications (not just databases!) that read and write files, particularly in transactional contexts where you actually care about the integrity of the data and when you are editing data in place (versus copy-on-write for example).

We'll go into a few scenarios where the following can happen:

• Data you write never actually makes it to disk
• Data you write get sent to the wrong location on disk
• Data you read is read from the wrong location on disk
• Data gets corrupted on disk

And how real-world data systems think about these scenarios. (They don't always think of them at all!)

If I don't say otherwise I'm talking about behavior on Linux.

The post is largely a review of two papers: Parity Lost and Parity Regained and Characteristics, Impact, and Tolerance of Partial Disk Failures. These two papers also go into the frequency of some of the issues discussed here. These behaviors actually happen in real life!

Thank you to Alex Miller and George Xanthakis for reviewing a draft of this post.

Terminology

Some of these terms are reused in different contexts, and sometimes they are reused because they effectively mean the same thing in a certain configuration. But I'll try to be explicit to avoid confusion.

Sector

The smallest amount of data that can be read and written atomically by hardware. It used to be 512 bytes, but on modern disks it is often 4KiB. There doesn't seem to be any safe assumption you can make about sector size, despite file system defaults (see below). You must check your disks to know.

Block (filesystem/kernel view)

Typically set to the sector size since only this block size is atomic. The default in ext4 is 4KiB.

Page (kernel view)

A disk block that is in memory. Any reads/writes less than the size of a block will read the entire block into kernel memory even if less than that amount is sent back to userland.

Page (database/application view)

The smallest amount of data the system (database, application, etc.) chooses to act on, when it's read or written or held in memory. The page size is some multiple of the filesystem/kernel block size (including the multiple being 1). SQLite's default page size is 4KiB. MySQL's default page size is 16KiB. Postgres's default page size is 8KiB.

Things that go wrong

The data didn't reach disk

By default, file writes succeed when the data is copied into kernel memory (buffered IO). The man page for write(2) says:

A successful return from write() does not make any guarantee that data has been committed to disk. On some filesystems, including NFS, it does not even guarantee that space has successfully been reserved for the data. In this case, some errors might be delayed until a future write(), fsync(2), or even close(2). The only way to be sure is to call fsync(2) after you are done writing all your data.

If you don't call fsync on Linux the data isn't necessarily durably on disk, and if the system crashes or restarts before the disk writes the data to non-volatile storage, you may lose data.

With O_DIRECT, file writes succeed when the data is copied to at least the disk cache. Alternatively you could open the file with O_DIRECT|O_SYNC (or O_DIRECT|O_DSYNC) and forgo fsync calls.

fsync on macOS is a no-op.

If you're confused, read Userland Disk I/O.

Postgres, SQLite, MongoDB, MySQL fsync data before considering a transaction successful by default. RocksDB does not.

The data was fsynced but fsync failed

fsync isn't guaranteed to succeed. And when it fails you can't tell which write failed. It may not even be a failure of a write to a file that your process opened:

Ideally, the kernel would report errors only on file descriptions on which writes were done that subsequently failed to be written back. The generic pagecache infrastructure does not track the file descriptions that have dirtied each individual page however, so determining which file descriptors should get back an error is not possible.

Instead, the generic writeback error tracking infrastructure in the kernel settles for reporting errors to fsync on all file descriptions that were open at the time that the error occurred. In a situation with multiple writers, all of them will get back an error on a subsequent fsync, even if all of the writes done through that particular file descriptor succeeded (or even if there were no writes on that file descriptor at all).

Don't be 2018-era Postgres.

The only way to have known which exact write failed would be to open the file with O_DIRECT|O_SYNC (or O_DIRECT|O_DSYNC), though this is not the only way to handle fsync failures.

The data was corrupted

If you don't checksum your data on write and check the checksum on read (as well as periodic scrubbing a la ZFS) you will never be aware if and when the data gets corrupted and you will have to restore (who knows how far back in time) from backups if and when you notice.

ZFS, MongoDB (WiredTiger), MySQL (InnoDB), and RocksDB checksum data by default. Postgres and SQLite do not (though databases created from Postgres 18+ will).

You should probably turn on checksums on any system that supports it, regardless of the default.

The data was partially written

Only when the page size you write = block size of your filesystem = sector size of your disk is a write guaranteed to be atomic. If you need to write multiple sectors of data atomically there is the risk that some sectors are written and then the system crashes or restarts. This behavior is called torn writes or torn pages.

Postgres, SQLite, and MySQL (InnoDB) handle torn writes. Torn writes are by definition not relevant to immutable storage systems like RocksDB (and other LSM Tree or Copy-on-Write systems like MongoDB (WiredTiger)) unless writes (that update metadata) span sectors.

If your file system duplicates all writes like MySQL (InnoDB) does (like you can with data=journal in ext4) you may also not have to worry about torn writes. On the other hand, this amplifies writes 2x.

The data didn't reach disk, part 2

Sometimes fsync succeeds but the data isn't actually on disk because the disk is lying. This behavior is called lost writes or phantom writes. You can be resilient to phantom writes by always reading back what you wrote (expensive) or versioning what you wrote.

Databases and file systems generally do not seem to handle this situation.

The data was written to the wrong place, read from the wrong place

If you aren't including where data is supposed to be on disk as part of the checksum or page itself, you risk being unaware that you wrote data to the wrong place or that you read from the wrong place. This is called misdirected writes/reads.

Databases and file systems generally do not seem to handle this situation.

Further reading

In increasing levels of paranoia (laudatory) follow ZFS, Andrea and Remzi Arpaci-Dusseau, and TigerBeetle.

I wrote a post covering some of the scenarios you might want to be aware of, and resilient to, when you write systems that read and write files. pic.twitter.com/7FxbpMo1xm

— Phil Eaton (@eatonphil) March 27, 2025

For this demo, I'm using the Eurovision songs available at Kaggle, which contain lyrics in their original language, and translated into English

Sample data

I downloaded and uncompressed the files:

wget -c -O eurovision-song-lyrics.zip eurovision-song-lyrics.zip https://www.kaggle.com/api/v1/datasets/download/minitree/eurovision-song-lyrics 
  unzip -o eurovision-song-lyrics.zip 
  rm -f    eurovision-song-lyrics.zip

MongoDB Atlas and Shell

I install MongoDB Atlas CLI and start a local instance:

curl https://fastdl.mongodb.org/mongocli/mongodb-atlas-cli_1.41.1_linux_arm64.tar.gz | 
 tar -xzvf - &&
 alias atlas=$PWD/mongodb-atlas-cli_1.41.1_linux_arm64/bin/atlas

atlas deployments setup  atlas --type local --port 27017 --force

This runs MongoDB Atlas in a Docker container:

I also installed MongoDB Shell to connect and run JavaScript and Node.js:

curl https://downloads.mongodb.com/compass/mongosh-2.4.2-linux-arm64.tgz | 
 tar -xzvf - && 
 alias mongosh=$PWD/mongosh-2.4.2-linux-arm64/bin/mongosh                                                                      

Ollama Large Language Model

I need a local LLM model to generate embeddings from lyrics. To avoid relying on external services, I will use Ollama with the nomic-embed-text model, which I install locally:

curl -fsSL https://ollama.com/install.sh | sh

ollama pull nomic-embed-text

npm install ollama

mongosh

I've installed the Ollama module for node.js and started MongoDB Shell.

Load data into MongoDB

I load the files into an eurovision collection:

const fs = require('fs/promises');
async function loadJsonToMongoDB() {
 const fileContent = await fs.readFile('eurovision-lyrics-2023.json', 'utf8');
 const jsonData = JSON.parse(fileContent);
 const documents = Object.values(jsonData);
 const result = await db.eurovision.insertMany(documents);
}

db.eurovision.drop();
loadJsonToMongoDB();
db.eurovision.countDocuments();

Generate embeddings

I update the MongoDB collection to add embeddings, generated from the lyrics, and generating embeddings with Ollama:

const ollama = require("ollama"); // Ollama Node.js client

// Calculate embeddings
async function calculateEmbeddings(collection) {
  try {
    // Process each document and update the embedding
    const cursor = collection.find();
    let counter = 0;
    for await (const doc of cursor) {
      // Call the embedding API
      const data = {
        model: 'nomic-embed-text',
        prompt: doc["Lyrics translation"]
      };
      const { embedding } = await ollama.default.embeddings(data);
      // Update the document with the new embedding
      await collection.updateOne(
        { _id: doc._id },
        { $set: { embedding: embedding } }
      );
      counter++;
      console.log(`Added embeddings for ${doc.Year} ${doc.Country}`);
    }
  } catch (error) {
    console.error('Error:', error);
  }
}
calculateEmbeddings(db.eurovision);

This takes some time:

MongoDB Vector Search Index

I create a vector search index (Ollama nomic-embed-text, like BERT, has 768 dimensions):

db.eurovision.createSearchIndex(
   "vectorSearchOnLyrics",
   "vectorSearch",
   {
      fields: [
         {
            type: "vector",
            numDimensions: 768,
            path: "embedding",
            similarity: "cosine"
         }
      ]
   }
)

db.eurovision.getSearchIndexes()

Don't forget the name of the index, it will be used to query, and a wrong index name simply results on no results.

Aggregation pipeline with vector search

Here is the function I'll use to query with a prompt, converting the prompt to vector embedding with the same model:

const ollama = require("ollama"); // Ollama Node.js client

async function vectorSearch(collection, prompt) {
  try {
    // Get the embedding for the prompt
      const data = {
        model: 'nomic-embed-text',
        prompt: prompt,
      };
      const { embedding } = await ollama.default.embeddings(data);
    // Perform a vector search in aggregation pipeline
    const results=collection.aggregate([
      {
      "$vectorSearch": {
        "index": "vectorSearchOnLyrics",
        "path": "embedding",
        "queryVector": embedding,
        "numCandidates": 10,
        "limit": 5
      }
      },{
      "$project": {
        "Year": 1,
        "Country": 1,
        "Artist": 1,
        "Song": 1,
        "Language": 1,
        "score": { "$meta": "vectorSearchScore" }
       }
     }
     ]);
    // Display the result
    results.forEach(doc => {
      console.log(`${doc.score.toFixed(2)} ${doc.Year} ${doc.Country} ${doc.Artist} ${doc.Song} (${doc.Language})`);
    });
  } catch (error) {
    console.error('Error during vector search:', error);
  }
}

Here are a few prompts that I tried to find the songs for which I remember some bits, using different languages:

vectorSearch(db.eurovision,
 "un nino y un pájaro"
);

vectorSearch(db.eurovision,
 "a wax doll singing"
);

vectorSearch(db.eurovision,
 "Un chico llamado Mercy"
);

vectorSearch(db.eurovision,
 "lyrics were about being unique and not like the others"
);

Here are my results, the songs I had in mind appeared in the forst or second position:

Conclusion

This article discusses implementing vector search in a local MongoDB setup using Ollama and the Eurovision song lyrics dataset. We populated the database with vectorized data embedded in the documents and created a vector search index for retrieving songs based on semantic similarity.

Storing embeddings with data, rather than in a separate database, is advantageous because embeddings are generated from document fields and can be indexed like any other fields. MongoDB stores arrays natively with the document model. The next step will involve generating embeddings without moving data out of the database, by integrating Voyager AI into MongoDB.

For this demo, I'm using the Eurovision songs available at Kaggle, which contain lyrics in their original language, and translated into English

Sample data

I downloaded and uncompressed the files:

wget -c -O eurovision-song-lyrics.zip eurovision-song-lyrics.zip https://www.kaggle.com/api/v1/datasets/download/minitree/eurovision-song-lyrics 
  unzip -o eurovision-song-lyrics.zip 
  rm -f    eurovision-song-lyrics.zip

MongoDB Atlas and Shell

I install MongoDB Atlas CLI and start a local instance:

curl https://fastdl.mongodb.org/mongocli/mongodb-atlas-cli_1.41.1_linux_arm64.tar.gz | 
 tar -xzvf - &&
 alias atlas=$PWD/mongodb-atlas-cli_1.41.1_linux_arm64/bin/atlas

atlas deployments setup  atlas --type local --port 27017 --force

This runs MongoDB Atlas in a Docker container:

I also installed MongoDB Shell to connect and run JavaScript and Node.js:

curl https://downloads.mongodb.com/compass/mongosh-2.4.2-linux-arm64.tgz | 
 tar -xzvf - && 
 alias mongosh=$PWD/mongosh-2.4.2-linux-arm64/bin/mongosh                                                                      

Ollama Large Language Model

I need a local LLM model to generate embeddings from lyrics. To avoid relying on external services, I will use Ollama with the nomic-embed-text model, which I install locally:

curl -fsSL https://ollama.com/install.sh | sh

ollama pull nomic-embed-text

npm install ollama

mongosh

I've installed the Ollama module for node.js and started MongoDB Shell.

Load data into MongoDB

I load the files into an eurovision collection:

const fs = require('fs/promises');
async function loadJsonToMongoDB() {
 const fileContent = await fs.readFile('eurovision-lyrics-2023.json', 'utf8');
 const jsonData = JSON.parse(fileContent);
 const documents = Object.values(jsonData);
 const result = await db.eurovision.insertMany(documents);
}

db.eurovision.drop();
loadJsonToMongoDB();
db.eurovision.countDocuments();

Generate embeddings

I update the MongoDB collection to add embeddings, generated from the lyrics, and generating embeddings with Ollama:

const ollama = require("ollama"); // Ollama Node.js client

// Calculate embeddings
async function calculateEmbeddings(collection) {
  try {
    // Process each document and update the embedding
    const cursor = collection.find();
    let counter = 0;
    for await (const doc of cursor) {
      // Call the embedding API
      const data = {
        model: 'nomic-embed-text',
        prompt: doc["Lyrics translation"]
      };
      const { embedding } = await ollama.default.embeddings(data);
      // Update the document with the new embedding
      await collection.updateOne(
        { _id: doc._id },
        { $set: { embedding: embedding } }
      );
      counter++;
      console.log(`Added embeddings for ${doc.Year} ${doc.Country}`);
    }
  } catch (error) {
    console.error('Error:', error);
  }
}
calculateEmbeddings(db.eurovision);

This takes some time:

MongoDB Vector Search Index

I create a vector search index (Ollama nomic-embed-text, like BERT, has 768 dimensions):

db.eurovision.createSearchIndex(
   "vectorSearchOnLyrics",
   "vectorSearch",
   {
      fields: [
         {
            type: "vector",
            numDimensions: 768,
            path: "embedding",
            similarity: "cosine"
         }
      ]
   }
)

db.eurovision.getSearchIndexes()

Don't forget the name of the index, it will be used to query, and a wrong index name simply results on no results.

Aggregation pipeline with vector search

Here is the function I'll use to query with a prompt, converting the prompt to vector embedding with the same model:

const ollama = require("ollama"); // Ollama Node.js client

async function vectorSearch(collection, prompt) {
  try {
    // Get the embedding for the prompt
      const data = {
        model: 'nomic-embed-text',
        prompt: prompt,
      };
      const { embedding } = await ollama.default.embeddings(data);
    // Perform a vector search in aggregation pipeline
    const results=collection.aggregate([
      {
      "$vectorSearch": {
        "index": "vectorSearchOnLyrics",
        "path": "embedding",
        "queryVector": embedding,
        "numCandidates": 10,
        "limit": 5
      }
      },{
      "$project": {
        "Year": 1,
        "Country": 1,
        "Artist": 1,
        "Song": 1,
        "Language": 1,
        "score": { "$meta": "vectorSearchScore" }
       }
     }
     ]);
    // Display the result
    results.forEach(doc => {
      console.log(`${doc.score.toFixed(2)} ${doc.Year} ${doc.Country} ${doc.Artist} ${doc.Song} (${doc.Language})`);
    });
  } catch (error) {
    console.error('Error during vector search:', error);
  }
}

Here are a few prompts that I tried to find the songs for which I remember some bits, using different languages:

vectorSearch(db.eurovision,
 "un nino y un pájaro"
);

vectorSearch(db.eurovision,
 "a wax doll singing"
);

vectorSearch(db.eurovision,
 "Un chico llamado Mercy"
);

vectorSearch(db.eurovision,
 "lyrics were about being unique and not like the others"
);

Here are my results, the songs I had in mind appeared in the forst or second position:

Conclusion

This article discusses implementing vector search in a local MongoDB setup using Ollama and the Eurovision song lyrics dataset. We populated the database with vectorized data embedded in the documents and created a vector search index for retrieving songs based on semantic similarity.

Storing embeddings with data, rather than in a separate database, is advantageous because embeddings are generated from document fields and can be indexed like any other fields. MongoDB stores arrays natively with the document model. The next step will involve generating embeddings without moving data out of the database, by integrating Voyager AI into MongoDB.

This is an external post of mine. Click here if you are not redirected.

This paper (2024) presents Cedar, AWS's new authorization policy language. By providing a clear declarative way to manage access control policies, Cedar addresses the common limitations of embedding authorization logic directly into application code: problems with correctness, auditing, and maintainence. Cedar introduces a domain-specific language (DSL) to express policies that are separate from application code, and improves readability and manageability. In that sense, this is like aspect-oriented programming but for authorization policy.

The language is designed with four main objectives: expressiveness, performance, safety, and analyzability. Cedar balances these goals by supporting role-based (RBAC), attribute-based (ABAC), and relationship-based (ReBAC) access control models.

Policy Structure and Evaluation

Cedar policies consist of three primary components: effect, scope, and conditions. The effect can either be "permit" or "forbid", defining whether access is granted or denied. The scope specifies the principal (user), action, and resource. Conditions provide additional constraints using entity attributes.

Cedar ensures safety by applying a deny-by-default model. If no "permit" policy is applicable, access is denied. Additionally, "forbid" policies always take precedence over "permit" policies.

A schema defines entity types, attributes, and valid actions, and Cedar's policy validator uses optional typing and static capabilities to catch potential errors, like accessing non-existent attributes. Another key feature is policy templates, which allow developers to define reusable policy patterns with placeholders. These placeholders are instantiated with specific entities or attributes at runtime, reducing redundancy and simplifying policy management.

Symbolic Analysis and Proofs

Cedar supports deeper policy analysis through symbolic compilation. Symbolic compilation reduces policies to SMT (Satisfiability Modulo Theories) formulas, enabling automated reasoning about policy behavior. This allows checking for policy equivalence, identifying inconsistencies, and verifying security invariants.

Checking policy equivalence is particularly useful for ensuring that policy refactoring or updates do not introduce unintended behavior. By comparing two versions of a policy set, Cedar can determine if they produce identical authorization decisions for all possible requests. This is crucial for organizations with frequent policy updates to ensure no permissions are inadvertently granted or revoked.

By compiling to SMT, Cedar leverages the rapid advancements in SMT solvers over the past two decades. Improvements in solver algorithms, better heuristics for decision procedures, and more efficient memory management have significantly enhanced SMT solver performance. Tools like Z3 and CVC5 are now capable of solving complex logical formulas quickly, making real-time policy analysis feasible. These advancements enable Cedar to perform sophisticated policy checks with minimal overhead.

Cedar is formalized in the Lean programming language and uses its proof assistant to establish key properties like correctness of the authorization algorithm, sound slicing, and validation soundness. The compiler's encoding is proved to be decidable, sound, and complete. This result, the first of its kind for a non-trivial policy language, is made possible by Cedar's careful control over expressiveness and by leveraging invariants ensured by Cedar's policy validator.

Implementation and Evaluation

Cedar is implemented in Rust and open-sourced at https://github.com/cedar-policy/. The implementation is extensively tested using differential random testing to ensure correctness.

Cedar was evaluated against two prominent open-source, general-purpose authorization languages, OpenFGA and Rego, using three example sets of policies. The evaluation demonstrated that Cedar's policy slicing significantly reduces evaluation time. For large policy sets, Cedar achieved a 28.7x to 35.2x speedup over OpenFGA and a 42.8x to 80.8x speedup over Rego.

Unlike OpenFGA and Rego, which show scaling challenges with increased input sizes, Cedar maintains consistently low evaluation latency. In a simulated Google Drive authorization scenario, Cedar handled requests in a median time of 4.0 microseconds (𝜇s) with 5 Users/Groups/Documents/Folders, increasing only to 5.0𝜇s with 50 Users/Groups/Documents/Folders. Similarly, in a GitHub-like authorization scenario, Cedar maintained a median performance of around 11.0𝜇s across all input ranges. Even at the 99th percentile (p99), Cedar's response times remained low, with under 10𝜇s for Google Drive and under 20𝜇s for GitHub. Further validating its real-world applicability, Cedar is deployed at scale within Amazon Web Services, providing secure and efficient authorization for large and complex systems.

In a previous blog post, I explained how MongoDB TTL indexes work and their optimization to avoid fragmentation during scans. However, I didn’t cover the details of on-disk storage. A recent Reddit question is the occasion to explore this aspect further.

Reproducible example

Here is a small program that inserts documents in a loop, with a timestamp and some random text:

// random string to be not too friendly with compression
function getRandomString(length) {
  const characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789';
  let result = '';
  const charactersLength = characters.length;
  for (let i = 0; i < length; i++) {
    result += characters.charAt(Math.floor(Math.random() * charactersLength));
  }
  return result;
}
// MongoDB loop for inserting documents with a random string
while (true) {
  const doc = { text: getRandomString(1000), ts: new Date() };
  db.ttl.insertOne(doc);
  insertedCount++;
}

Before executing this, I ran a background function to display statistics every minute, including the number of records, memory usage, and disk size.

// Prints stats every minute
 let insertedCount = 0;
 let maxThroughput = 0;
 let maxCollectionCount = 0;
 let maxCollectionSize = 0;
 let maxStorageSize = 0;
 let maxTotalIndexSize = 0;
 setInterval(async () => {
      const stats = await db.ttl.stats();
      const throughput = insertedCount / 10; // assuming measure over 10 seconds
      const collectionCount = stats.count;
      const collectionSizeMB = stats.size / 1024 / 1024;
      const storageSizeMB = stats.storageSize / 1024 / 1024;
      const totalIndexSizeMB = stats.totalIndexSize / 1024 / 1024;
      maxThroughput = Math.max(maxThroughput, throughput);
      maxCollectionCount = Math.max(maxCollectionCount, collectionCount);
      maxCollectionSize = Math.max(maxCollectionSize, collectionSizeMB);
      maxStorageSize = Math.max(maxStorageSize, storageSizeMB);
      maxTotalIndexSize = Math.max(maxTotalIndexSize, totalIndexSizeMB);
      console.log(`Collection Name: ${stats.ns}
   Throughput:        ${throughput.toFixed(0).padStart(10)} docs/min (max: ${maxThroughput.toFixed(0)} docs/min)
   Collection Size:   ${collectionSizeMB.toFixed(0).padStart(10)} MB (max: ${maxCollectionSize.toFixed(0)} MB)
   Number of Records: ${collectionCount.toFixed(0).padStart(10)}     (max: ${maxCollectionCount.toFixed(0)} docs)
   Storage Size:      ${storageSizeMB.toFixed(0).padStart(10)}    MB (max: ${maxStorageSize.toFixed(0)} MB)
   Total Index Size:  ${totalIndexSizeMB.toFixed(0).padStart(10)} MB (max: ${maxTotalIndexSize.toFixed(0)} MB)`);
      insertedCount = 0;
 }, 60000); // every minute

I created the collection with a TTL index, which automatically expires data older than five minutes:

// TTL expire after 5 minutes
db.ttl.drop();
db.ttl.createIndex({ ts: 1 }, { expireAfterSeconds: 300 });

I let this running to see how the storage size evolves. Note that this was run in on MongoDB 7.0.16 (without the auto compact job that appeared in 8.0).

Output after 3 hours

The consistent insert rate, combined with TTL expiration, keeps the number of documents in the collection relatively stable. Deletions occur every minute, ensuring that the overall document count remains consistent.

I observe the same from the statistics I log every minute:

The storage size also remains constant, at 244MB for the table and 7MB for the indexes. The size of files has increased for the first 6 minutes and then remained constant:

This is sufficient to show that the deletion and insertion do not have a fragmentation effect that would require additional consideration. About 25% is marked as available for reuse and is effectively reused.

It is possible to force a compaction, to temporarily reclaim more space, but it is not necessary:

Collection Name: test.ttl
   Throughput:              3286 docs/min (max: 3327 docs/min)
   Collection Size:          170 MB (max: 198 MB)
   Number of Records:     171026     (max: 198699 docs)
   Storage Size:             244    MB (max: 244 MB)
   Total Index Size:           7 MB (max: 7 MB)

Collection Name: test.ttl
   Throughput:              3299 docs/min (max: 3327 docs/min)
   Collection Size:          170 MB (max: 198 MB)
   Number of Records:     170977     (max: 198699 docs)
   Storage Size:             244    MB (max: 244 MB)
   Total Index Size:           6 MB (max: 7 MB)

Collection Name: test.ttl
   Throughput:              3317 docs/min (max: 3327 docs/min)
   Collection Size:          170 MB (max: 198 MB)
   Number of Records:     170985     (max: 198699 docs)
   Storage Size:             244    MB (max: 244 MB)
   Total Index Size:           6 MB (max: 7 MB)

test> db.runCommand({ compact: 'ttl' });
{ bytesFreed: 49553408, ok: 1 }

Collection Name: test.ttl
   Throughput:              1244 docs/min (max: 3327 docs/min)
   Collection Size:          150 MB (max: 198 MB)
   Number of Records:     150165     (max: 198699 docs)
   Storage Size:             197    MB (max: 244 MB)
   Total Index Size:           6 MB (max: 7 MB)
Collection Name: test.ttl
   Throughput:              3272 docs/min (max: 3327 docs/min)
   Collection Size:          149 MB (max: 198 MB)
   Number of Records:     149553     (max: 198699 docs)
   Storage Size:             203    MB (max: 244 MB)
   Total Index Size:           6 MB (max: 7 MB)

While this has reduced storage, it eventually returns to its normal volume. It's typical for a B-tree to maintain some free space, which helps to minimize frequent space allocations and reclaim.

Here is a focus when I've run manual compaction:

Conclusion

TTL deletion makes space available for reuse instead of reclaiming it immediately, but this doesn't increase the fragmentation. This space is reused automatically to maintain a total size proportional to the document count, with a constant free space of 25% in my case, to minimize frequent allocations typical of B-Tree implementations.
The TTL mechanism operates autonomously, requiring no manual compaction. If you have any doubt, monitor it. MongoDB offers statistics to compare logical and physical sizes at both the MongoDB layer and WiredTiger storage.

In a previous blog post, I explained how MongoDB TTL indexes work and their optimization to avoid fragmentation during scans. However, I didn’t cover the details of on-disk storage. A recent Reddit question is the occasion to explore this aspect further.

Reproducible example

Here is a small program that inserts documents in a loop, with a timestamp and some random text:

// random string to be not too friendly with compression
function getRandomString(length) {
  const characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789';
  let result = '';
  const charactersLength = characters.length;
  for (let i = 0; i < length; i++) {
    result += characters.charAt(Math.floor(Math.random() * charactersLength));
  }
  return result;
}
// MongoDB loop for inserting documents with a random string
while (true) {
  const doc = { text: getRandomString(1000), ts: new Date() };
  db.ttl.insertOne(doc);
  insertedCount++;
}

Before executing this, I ran a background function to display statistics every minute, including the number of records, memory usage, and disk size.

// Prints stats every minute
 let insertedCount = 0;
 let maxThroughput = 0;
 let maxCollectionCount = 0;
 let maxCollectionSize = 0;
 let maxStorageSize = 0;
 let maxTotalIndexSize = 0;
 setInterval(async () => {
      const stats = await db.ttl.stats();
      const throughput = insertedCount / 10; // assuming measure over 10 seconds
      const collectionCount = stats.count;
      const collectionSizeMB = stats.size / 1024 / 1024;
      const storageSizeMB = stats.storageSize / 1024 / 1024;
      const totalIndexSizeMB = stats.totalIndexSize / 1024 / 1024;
      maxThroughput = Math.max(maxThroughput, throughput);
      maxCollectionCount = Math.max(maxCollectionCount, collectionCount);
      maxCollectionSize = Math.max(maxCollectionSize, collectionSizeMB);
      maxStorageSize = Math.max(maxStorageSize, storageSizeMB);
      maxTotalIndexSize = Math.max(maxTotalIndexSize, totalIndexSizeMB);
      console.log(`Collection Name: ${stats.ns}
   Throughput:        ${throughput.toFixed(0).padStart(10)} docs/min (max: ${maxThroughput.toFixed(0)} docs/min)
   Collection Size:   ${collectionSizeMB.toFixed(0).padStart(10)} MB (max: ${maxCollectionSize.toFixed(0)} MB)
   Number of Records: ${collectionCount.toFixed(0).padStart(10)}     (max: ${maxCollectionCount.toFixed(0)} docs)
   Storage Size:      ${storageSizeMB.toFixed(0).padStart(10)}    MB (max: ${maxStorageSize.toFixed(0)} MB)
   Total Index Size:  ${totalIndexSizeMB.toFixed(0).padStart(10)} MB (max: ${maxTotalIndexSize.toFixed(0)} MB)`);
      insertedCount = 0;
 }, 60000); // every minute

I created the collection with a TTL index, which automatically expires data older than five minutes:

// TTL expire after 5 minutes
db.ttl.drop();
db.ttl.createIndex({ ts: 1 }, { expireAfterSeconds: 300 });

I let this running to see how the storage size evolves. Note that this was run in on MongoDB 7.0.16 (without the auto compact job that appeared in 8.0).

Output after 3 hours

The consistent insert rate, combined with TTL expiration, keeps the number of documents in the collection relatively stable. Deletions occur every minute, ensuring that the overall document count remains consistent.

I observe the same from the statistics I log every minute:

The storage size also remains constant, at 244MB for the table and 7MB for the indexes. The size of files has increased for the first 6 minutes and then remained constant:

This is sufficient to show that the deletion and insertion do not have a fragmentation effect that would require additional consideration. About 25% is marked as available for reuse and is effectively reused.

It is possible to force a compaction, to temporarily reclaim more space, but it is not necessary:

Collection Name: test.ttl
   Throughput:              3286 docs/min (max: 3327 docs/min)
   Collection Size:          170 MB (max: 198 MB)
   Number of Records:     171026     (max: 198699 docs)
   Storage Size:             244    MB (max: 244 MB)
   Total Index Size:           7 MB (max: 7 MB)

Collection Name: test.ttl
   Throughput:              3299 docs/min (max: 3327 docs/min)
   Collection Size:          170 MB (max: 198 MB)
   Number of Records:     170977     (max: 198699 docs)
   Storage Size:             244    MB (max: 244 MB)
   Total Index Size:           6 MB (max: 7 MB)

Collection Name: test.ttl
   Throughput:              3317 docs/min (max: 3327 docs/min)
   Collection Size:          170 MB (max: 198 MB)
   Number of Records:     170985     (max: 198699 docs)
   Storage Size:             244    MB (max: 244 MB)
   Total Index Size:           6 MB (max: 7 MB)

test> db.runCommand({ compact: 'ttl' });
{ bytesFreed: 49553408, ok: 1 }

Collection Name: test.ttl
   Throughput:              1244 docs/min (max: 3327 docs/min)
   Collection Size:          150 MB (max: 198 MB)
   Number of Records:     150165     (max: 198699 docs)
   Storage Size:             197    MB (max: 244 MB)
   Total Index Size:           6 MB (max: 7 MB)
Collection Name: test.ttl
   Throughput:              3272 docs/min (max: 3327 docs/min)
   Collection Size:          149 MB (max: 198 MB)
   Number of Records:     149553     (max: 198699 docs)
   Storage Size:             203    MB (max: 244 MB)
   Total Index Size:           6 MB (max: 7 MB)

While this has reduced storage, it eventually returns to its normal volume. It's typical for a B-tree to maintain some free space, which helps to minimize frequent space allocations and reclaim.

Here is a focus when I've run manual compaction:

Conclusion

TTL deletion makes space available for reuse instead of reclaiming it immediately, but this doesn't increase the fragmentation. This space is reused automatically to maintain a total size proportional to the document count, with a constant free space of 25% in my case, to minimize frequent allocations typical of B-Tree implementations.
The TTL mechanism operates autonomously, requiring no manual compaction. If you have any doubt, monitor it. MongoDB offers statistics to compare logical and physical sizes at both the MongoDB layer and WiredTiger storage.