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    TigerFS: A Filesystem Backed by PostgreSQL
    postgres

    TigerFS: A Filesystem Backed by PostgreSQL

    Polliog April 9, 2026
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    TigerFS is a filesystem backed by PostgreSQL, built by the Timescale team. It mounts a database as a...

    TigerFS is a filesystem backed by PostgreSQL, built by the Timescale team. It mounts a database as a local directory via FUSE on Linux and NFS on macOS. Every file is a real row. Every directory is a table. Writes are transactions. Multiple processes and machines can read and write concurrently with full ACID guarantees.

    There are two distinct ways to use it.

    # Install (Linux requires fuse3; macOS needs no extra dependencies)
    curl -fsSL https://install.tigerfs.io | sh
    
    # Mount any PostgreSQL database
    tigerfs mount postgres://localhost/mydb /mnt/db
    

    Mode 1: Data-First

    Mount any existing PostgreSQL database and explore it with standard UNIX tools. Every path resolves to optimized SQL that gets pushed down to the database.

    Exploring

    ls /mnt/db/                                          # list tables
    ls /mnt/db/users/                                    # list rows by primary key
    cat /mnt/db/users/123.json                           # read a row as JSON
    cat /mnt/db/users/123/email.txt                      # read a single column
    cat /mnt/db/users/.by/email/[email protected]   # lookup by indexed column
    

    Modifying

    echo '[email protected]' > /mnt/db/users/123/email.txt          # update a column
    echo '{"email":"[email protected]","name":"A"}' > /mnt/db/users/123.json  # PATCH via JSON
    mkdir /mnt/db/users/456                                         # insert a row
    rm -r /mnt/db/users/456/                                        # delete a row
    

    Pipeline Queries

    Filters, ordering, and pagination can be chained directly in the path. TigerFS executes the whole chain as a single SQL query:

    # Last 10 orders for customer 123, sorted by created_at, as JSON
    cat /mnt/db/orders/.by/customer_id/123/.order/created_at/.last/10/.export/json
    
    # Shipped orders, specific columns only, as CSV
    cat /mnt/db/orders/.filter/status/shipped/.columns/id,total,created_at/.export/csv
    

    Available segments (chainable in any order):

    SegmentDescription
    .by/col/valIndexed filter
    .filter/col/valAny column filter
    .order/colSort
    .columns/a,b,cColumn projection
    .first/N, .last/N, .sample/NPagination
    .export/json|csv|tsvOutput format

    Bulk Ingest

    cat data.csv > /mnt/db/orders/.import/.append/csv    # append rows
    cat data.csv > /mnt/db/orders/.import/.sync/csv      # upsert by primary key
    cat data.csv > /mnt/db/orders/.import/.overwrite/csv # replace the table
    

    Schema Management

    Tables, indexes, and views are managed through a staging pattern:

    mkdir /mnt/db/.create/orders
    echo "CREATE TABLE orders (...)" > /mnt/db/.create/orders/sql
    touch /mnt/db/.create/orders/.commit
    

    Mode 2: File-First

    Create a new database and use it as a transactional shared workspace. Any tool that works with files works here: AI agents, grep, vim, shell scripts.

    Markdown Apps

    "Apps" define how TigerFS presents a table as a native file format. Writing markdown to .build/ turns a table into a directory of .md files where YAML frontmatter maps to columns and the document body maps to a text column:

    echo "markdown" > /mnt/db/.build/blog
    
    cat > /mnt/db/blog/hello-world.md << 'EOF'
    ---
    title: Hello World
    author: alice
    tags: [intro]
    ---
    
    # Hello World
    
    Welcome to my blog...
    EOF
    
    # Standard tools work as expected
    grep -l "author: alice" /mnt/db/blog/*.md
    mkdir /mnt/db/blog/tutorials
    mv /mnt/db/blog/hello-world.md /mnt/db/blog/tutorials/
    

    Version History

    Add history to the app definition and every edit is captured as a timestamped snapshot in a read-only .history/ directory. History uses TimescaleDB hypertables for compressed storage and tracks files across renames via stable row UUIDs:

    echo "markdown,history" > /mnt/db/.build/notes
    
    ls /mnt/db/notes/.history/hello.md/
    # 2026-02-24T150000Z  2026-02-12T013000Z
    
    cat /mnt/db/notes/.history/hello.md/2026-02-12T013000Z
    

    Multi-Agent Task Queue

    mv between directories is an atomic database operation. Two agents cannot claim the same task because the underlying transaction will fail for one of them - no distributed lock manager, no coordination API needed:

    echo "markdown,history" > /mnt/db/.build/tasks
    mkdir /mnt/db/tasks/todo /mnt/db/tasks/doing /mnt/db/tasks/done
    
    cat > /mnt/db/tasks/todo/fix-auth-bug.md << 'EOF'
    ---
    priority: high
    assigned_to:
    ---
    The OAuth token refresh is failing for users with...
    EOF
    
    # Agent claims the task - atomic database operation
    mv /mnt/db/tasks/todo/fix-auth-bug.md /mnt/db/tasks/doing/fix-auth-bug.md
    
    # Agent marks it done
    mv /mnt/db/tasks/doing/fix-auth-bug.md /mnt/db/tasks/done/fix-auth-bug.md
    
    # Check what is in progress
    ls /mnt/db/tasks/doing/
    grep "assigned_to:" /mnt/db/tasks/doing/*.md
    

    Shared Agent Workspace

    Multiple agents on different machines can read and write the same files concurrently. Changes are visible immediately with no pull, push, or merge step:

    # Agent A writes findings
    cat > /mnt/db/kb/auth-analysis.md << 'EOF'
    ---
    author: agent-a
    ---
    OAuth 2.0 is the recommended approach because...
    EOF
    
    # Agent B reads immediately, no sync needed
    cat /mnt/db/kb/auth-analysis.md
    
    # Agent B updates the document
    cat > /mnt/db/kb/auth-analysis.md << 'EOF'
    ---
    author: agent-a
    reviewed-by: agent-b
    status: approved
    ---
    OAuth 2.0 is the recommended approach because... [approved with comments]
    EOF
    
    # Full edit trail
    ls /mnt/db/kb/.history/auth-analysis.md/
    

    Cloud Backends

    TigerFS works with any PostgreSQL database via connection string. It also integrates with Timescale Cloud and Ghost through their CLIs - no passwords stored in config:

    tigerfs mount postgres://user:pass@host/mydb /mnt/db
    
    tigerfs mount tiger:abcde12345 /mnt/db   # Timescale Cloud
    tigerfs mount ghost:fghij67890 /mnt/db   # Ghost
    
    # Fork a database for safe experimentation
    tigerfs fork /mnt/db my-experiment
    

    Why the File Interface

    The point of the filesystem abstraction is that every tool already speaks it. grep, awk, jq, shell scripts, AI coding agents (Claude Code, Cursor, and others) all understand files without any SDK, schema definition, or client library to set up.

    For multi-agent coordination specifically:

    ApproachWhat you build on top
    Custom REST APIEndpoints, auth, deployment
    Shared database directlySQL or ORM, schema definitions, client libraries
    GitPull/push/merge workflow, conflict resolution
    S3No transactions, no structured queries
    TigerFSMount and use standard tools

    The coordination logic - atomic task claims, version history, concurrent access - lives in PostgreSQL. The application doesn't implement it.


    Current Status

    TigerFS is at v0.5.0 and described as early-stage by the team, though the core design is stable. The data-first mode is functional today for any PostgreSQL database. Planned additions include support for tables without primary keys (read-only via ctid) and TimescaleDB hypertable time-based navigation.

    GitHub: timescale/tigerfs - Docs: tigerfs.io

    tigerfs mount postgres://localhost/yourdb /mnt/db
    ls /mnt/db/
    

    Tags

    postgresdatabasedevtoolsai

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