# Understanding CRDTs in Automerge

    library(automerge)

## What are CRDTs?

Conflict-free Replicated Data Types (CRDTs) are data structures that can be replicated across multiple computers and modified independently without coordination. The key property is that when all changes are eventually synchronized, all replicas converge to the same state automatically - without manual conflict resolution.

### The Problem CRDTs Solve

Consider a simple example without CRDTs:

    Initial state: counter = 0

    Alice's device (offline): counter = counter + 1  → counter = 1
    Bob's device (offline):   counter = counter + 2  → counter = 2

    When they sync:
    - If we use "last write wins": counter = 2 (Alice's change lost!)
    - If we use "first write wins": counter = 1 (Bob's change lost!)
    - Manual conflict resolution: Ask user to choose (annoying!)

With a CRDT counter:

    Initial state: counter = 0

    Alice's device: increment(1)
    Bob's device:   increment(2)

    When they sync:
    - CRDT automatically merges: counter = 3 (both changes preserved!)

### CRDT Properties

All CRDTs have these properties:

1. Associativity: The order of merging doesn’t matter
   - `merge(merge(A, B), C) = merge(A, merge(B, C))`
2. Commutativity: Who merges with whom doesn’t matter
   - `merge(A, B) = merge(B, A)`
3. Idempotency: Merging the same change multiple times has no additional effect
   - `merge(A, A) = A`

These properties ensure strong eventual consistency: all devices that see the same set of changes will converge to the same state, regardless of network delays, message reordering, or duplicates.

## Automerge CRDT Types

Automerge implements several CRDT types, each optimized for different use cases.

### Register Values (Strings, Numbers, Booleans)

The simplest type: when two replicas concurrently write different values to the same key, one value wins. Automerge uses a deterministic conflict resolution algorithm (based on Lamport timestamps and actor IDs) to ensure all replicas converge to the same value.

    doc1 <- am_create()
    doc1[["name"]] <- "Alice"
    doc1[["score"]] <- 100
    am_commit(doc1)

    doc2 <- am_fork(doc1)

    # Concurrent edits
    doc1[["name"]] <- "Alice Smith"
    doc2[["name"]] <- "Alice Johnson"

    # Merge
    am_merge(doc1, doc2)

    # One value wins (deterministic, all replicas agree)
    doc1[["name"]]
    #> [1] "Alice Smith"

    # To see all conflicting values (not just the winner), use am_map_get_all()
    all_values <- am_map_get_all(doc1, AM_ROOT, "name")
    all_values
    #> [[1]]
    #> [1] "Alice Johnson"
    #>
    #> [[2]]
    #> [1] "Alice Smith"

    am_close(doc1)
    am_close(doc2)

When to use: Simple values where automatic conflict resolution is acceptable (metadata, settings, labels).

Trade-off: One concurrent change is lost, but resolution is deterministic and all replicas converge to the same state. Use `am_map_get_all()` or `am_list_get_all()` when you need to surface conflicts to users.

### Maps (Nested Objects)

Maps are collections of key-value pairs. Each key uses deterministic conflict resolution (one value wins), but different keys can be edited concurrently without conflict.

    doc3 <- am_create()
    doc3[["user"]] <- list(name = "Alice", age = 30L, city = "Boston")
    am_commit(doc3)

    doc4 <- am_fork(doc3)

    # Concurrent edits to different keys
    user3 <- am_get(doc3, AM_ROOT, "user")
    am_put(doc3, user3, "age", 31L)

    user4 <- am_get(doc4, AM_ROOT, "user")
    am_put(doc4, user4, "city", "New York")

    # Merge - both changes preserved
    am_merge(doc3, doc4)

    # Both edits are present
    user_final <- am_get(doc3, AM_ROOT, "user")
    am_get(doc3, user_final, "age")
    #> [1] 31
    am_get(doc3, user_final, "city")
    #> [1] "New York"

    am_close(doc3)
    am_close(doc4)

When to use: Structured data with multiple independent fields (user profiles, configuration objects).

Merge behavior: Changes to different keys merge cleanly. Changes to the same key use deterministic conflict resolution.

### Lists (Ordered Sequences)

Lists use a sophisticated CRDT algorithm that preserves insertion order and handles concurrent insertions elegantly.

    doc5 <- am_create()
    am_put(doc5, AM_ROOT, "items", AM_OBJ_TYPE_LIST)
    items5 <- am_get(doc5, AM_ROOT, "items")

    am_put(doc5, items5, "end", "A")
    am_put(doc5, items5, "end", "C")
    am_commit(doc5)

    doc6 <- am_fork(doc5)

    # Concurrent insertions at same position
    items6 <- am_get(doc6, AM_ROOT, "items")
    am_insert(doc5, items5, 2, "B1") # Insert between A and C
    am_insert(doc6, items6, 2, "B2") # Insert between A and C

    # Merge
    am_merge(doc5, doc6)

    # Both insertions preserved with deterministic ordering
    for (i in seq_len(am_length(doc5, items5))) {
      print(am_get(doc5, items5, i))
    }
    #> [1] "A"
    #> [1] "B2"
    #> [1] "B1"
    #> [1] "C"

    am_close(doc5)
    am_close(doc6)

When to use: Ordered collections (task lists, sequences, timelines).

Merge behavior: Concurrent insertions at the same position are both preserved. The algorithm ensures deterministic ordering based on actor IDs.

Limitation: There are cases where insertion order is not perfectly preserved, primarily when inserting elements in reverse order. However, the algorithm performs well in most common scenarios.

### Inspecting Conflicts

Automerge always picks a single winner for concurrent edits to the same key or index, but both values are retained internally. Use `am_map_get_all()` and `am_list_get_all()` to inspect all conflicting values and present them to users or apply application-specific resolution logic.

    # Map conflict: two peers edit the same key concurrently
    doc_c1 <- am_create()
    doc_c1[["status"]] <- "draft"
    am_commit(doc_c1)

    doc_c2 <- am_fork(doc_c1)

    doc_c1[["status"]] <- "published"
    am_commit(doc_c1)
    doc_c2[["status"]] <- "archived"
    am_commit(doc_c2)

    am_merge(doc_c1, doc_c2)

    # am_get returns the winner
    am_get(doc_c1, AM_ROOT, "status")
    #> [1] "published"

    # am_map_get_all returns all conflicting values
    all_statuses <- am_map_get_all(doc_c1, AM_ROOT, "status")
    all_statuses
    #> [[1]]
    #> [1] "archived"
    #>
    #> [[2]]
    #> [1] "published"

The same approach works for lists — when two peers concurrently update the same index, `am_list_get_all()` reveals both values:

    # List conflict: two peers update the same index
    doc_l1 <- am_create()
    am_put(doc_l1, AM_ROOT, "scores", AM_OBJ_TYPE_LIST)
    scores <- am_get(doc_l1, AM_ROOT, "scores")
    am_insert(doc_l1, scores, 1, 100L)
    am_commit(doc_l1)

    doc_l2 <- am_fork(doc_l1)
    scores2 <- am_get(doc_l2, AM_ROOT, "scores")

    am_put(doc_l1, scores, 1, 200L)
    am_commit(doc_l1)
    am_put(doc_l2, scores2, 1, 300L)
    am_commit(doc_l2)

    am_merge(doc_l1, doc_l2)

    # Winner
    am_get(doc_l1, scores, 1)
    #> [1] 200

    # All conflicting values
    am_list_get_all(doc_l1, scores, 1)
    #> [[1]]
    #> [1] 300
    #>
    #> [[2]]
    #> [1] 200

    am_close(doc_c1)
    am_close(doc_c2)
    am_close(doc_l1)
    am_close(doc_l2)

### Text CRDTs (Collaborative Text Editing)

Text objects are optimized for character-level collaborative editing. They handle concurrent insertions, deletions, and edits gracefully.

    doc7 <- am_create()
    am_put(doc7, AM_ROOT, "document", am_text("The quick fox jumps"))
    am_commit(doc7)

    doc8 <- am_fork(doc7)

    text7 <- am_get(doc7, AM_ROOT, "document")
    text8 <- am_get(doc8, AM_ROOT, "document")

    # Concurrent edits (0-based inter-character positions)
    am_text_splice(text7, 10, 0, "brown ") # Insert "brown " at position 10
    am_text_splice(text8, 19, 0, " high") # Insert " high" at position 19 (end)

    # Merge
    am_merge(doc7, doc8)

    # Both edits preserved
    am_text_content(text7)
    #> [1] "The quick brown fox jumps high"

    am_close(doc7)
    am_close(doc8)

When to use: Collaborative documents, shared notes, code editors, chat messages.

Merge behavior: Character-level operations merge intelligently. Positions are tracked using stable identifiers, so concurrent edits at different positions don’t interfere.

Important: Regular strings (`AM_VAL_TYPE_STR`) use deterministic conflict resolution (one value wins). Only text objects (`AM_OBJ_TYPE_TEXT`) provide character-level CRDT merging.

    # String (deterministic conflict resolution)
    doc9 <- am_create()
    doc9[["title"]] <- "Document"
    doc10 <- am_fork(doc9)

    doc9[["title"]] <- "My Document"
    doc10[["title"]] <- "Our Document"
    am_merge(doc9, doc10)
    doc9[["title"]] # One value wins deterministically
    #> [1] "My Document"

    am_close(doc9)
    am_close(doc10)

    # Text object (CRDT)
    doc11 <- am_create()
    am_put(doc11, AM_ROOT, "content", am_text("Hello"))
    doc12 <- am_fork(doc11)

    text11 <- am_get(doc11, AM_ROOT, "content")
    text12 <- am_get(doc12, AM_ROOT, "content")

    am_text_splice(text11, 5, 0, " World")
    am_text_splice(text12, 5, 0, " Everyone")
    am_merge(doc11, doc12)

    am_text_content(text11)
    #> [1] "Hello Everyone World"

    am_close(doc11)
    am_close(doc12)

### Counters

Counters are a classic CRDT that adds up increments from all replicas.

    doc13 <- am_create()
    am_put(doc13, AM_ROOT, "likes", am_counter(0))
    am_commit(doc13)

    doc14 <- am_fork(doc13)

    # Concurrent increments
    am_counter_increment(doc13, AM_ROOT, "likes", 3)
    am_counter_increment(doc14, AM_ROOT, "likes", 5)
    am_counter_increment(doc14, AM_ROOT, "likes", -1)

    # Merge
    am_merge(doc13, doc14)

    # Sum of all increments
    doc13[["likes"]]
    #> <Automerge Counter: 7 >

    am_close(doc13)
    am_close(doc14)

When to use: Vote counts, like counts, inventory quantities, collaborative counters.

Merge behavior: All increments (positive or negative) are summed automatically.

### Timestamps

Timestamps record when a value was set. They use deterministic conflict resolution semantics (one value wins).

    doc15 <- am_create()
    doc15[["created_at"]] <- Sys.time()
    am_commit(doc15)

    Sys.sleep(0.1)

    doc16 <- am_fork(doc15)

    doc15[["updated_at"]] <- Sys.time()
    doc16[["updated_at"]] <- Sys.time()

    am_merge(doc15, doc16)

    doc15[["created_at"]]
    #> [1] "2026-02-26 08:39:50 GMT"
    doc15[["updated_at"]]
    #> [1] "2026-02-26 08:39:50 GMT"

    am_close(doc15)
    am_close(doc16)

When to use: Audit trails, modification times, temporal metadata.

## Advanced Text Features

### Cursors (Stable Position Tracking)

Cursors maintain stable positions in text even as the document is edited.

    doc17 <- am_create()
    am_put(doc17, AM_ROOT, "text", am_text("Hello World"))
    text17 <- am_get(doc17, AM_ROOT, "text")

    # Create cursor at position 6 (0-based: after "Hello ")
    cursor <- am_cursor(text17, 6)

    # Insert text before cursor
    am_text_splice(text17, 0, 0, "Hi ")

    # Cursor automatically adjusts
    new_pos <- am_cursor_position(cursor)
    new_pos # Cursor moved with text from original position 6
    #> [1] 9

Cursors can be serialized for persistence across sessions, and compared for equality:

    # Serialize to bytes or string
    cursor_bytes <- am_cursor_to_bytes(cursor)
    cursor_str <- am_cursor_to_string(cursor)

    # Restore later (requires the text object)
    restored <- am_cursor_from_bytes(cursor_bytes, text17)
    am_cursor_position(restored)
    #> [1] 9

    am_cursor_equal(cursor, restored) # TRUE
    #> [1] TRUE

    am_close(doc17)

When to use: Text editors (cursor tracking), collaborative commenting (position references), saving editor state across sessions.

### Marks (Text Formatting)

Marks attach metadata to text ranges, useful for formatting and annotations.

    doc18 <- am_create()
    am_put(doc18, AM_ROOT, "text", am_text("Hello World"))
    text18 <- am_get(doc18, AM_ROOT, "text")

    # Mark "Hello" as bold (positions 0-4, 0-based)
    am_mark(text18, 0, 5, "bold", TRUE, expand = "none")

    # Mark "World" as italic (positions 6-10)
    am_mark(text18, 6, 11, "italic", TRUE, expand = "none")

    # Query marks
    marks <- am_marks(text18)
    str(marks)
    #> List of 2
    #>  $ :List of 4
    #>   ..$ name : chr "bold"
    #>   ..$ value: logi TRUE
    #>   ..$ start: int 0
    #>   ..$ end  : int 5
    #>  $ :List of 4
    #>   ..$ name : chr "italic"
    #>   ..$ value: logi TRUE
    #>   ..$ start: int 6
    #>   ..$ end  : int 11

    # Marks at specific position
    marks_at_pos <- am_marks_at(text18, 2) # Position 2 (in "Hello")
    str(marks_at_pos)
    #> List of 1
    #>  $ :List of 4
    #>   ..$ name : chr "bold"
    #>   ..$ value: logi TRUE
    #>   ..$ start: int 0
    #>   ..$ end  : int 5

    am_close(doc18)

To remove a mark from a text range, use `am_mark_clear()`:

    doc18b <- am_create()
    am_put(doc18b, AM_ROOT, "text", am_text("Hello World"))
    text18b <- am_get(doc18b, AM_ROOT, "text")

    am_mark(text18b, 0, 11, "bold", TRUE)
    am_marks(text18b)
    #> [[1]]
    #> [[1]]$name
    #> [1] "bold"
    #>
    #> [[1]]$value
    #> [1] TRUE
    #>
    #> [[1]]$start
    #> [1] 0
    #>
    #> [[1]]$end
    #> [1] 11

    am_mark_clear(text18b, 0, 11, "bold")
    am_marks(text18b)
    #> list()

    am_close(doc18b)

When to use: Rich text editors, collaborative annotations, syntax highlighting.

Mark expansion: Controls how marks behave when text is inserted at boundaries: - `"none"`: Mark doesn’t expand - `"before"`: Expands to include text inserted before start - `"after"`: Expands to include text inserted after end - `"both"`: Expands at both boundaries

    doc19 <- am_create()
    am_put(doc19, AM_ROOT, "text", am_text("Hello"))
    text19 <- am_get(doc19, AM_ROOT, "text")

    # Mark with expansion
    am_mark(text19, 0, 5, "bold", TRUE, expand = "after")

    # Insert at end of mark
    am_text_splice(text19, 5, 0, " World")

    # Mark expands to include "World"
    marks <- am_marks(text19)
    str(marks)
    #> List of 1
    #>  $ :List of 4
    #>   ..$ name : chr "bold"
    #>   ..$ value: logi TRUE
    #>   ..$ start: int 0
    #>   ..$ end  : int 11

    am_close(doc19)

## CRDT Design Trade-offs

### Storage Growth

CRDTs preserve operation history to enable merging. This means document size grows with the number of operations, not just current content size.

    doc20 <- am_create()

    # Make many edits
    for (i in 1:100) {
      doc20[[paste0("key", i)]] <- i
    }
    am_commit(doc20)

    # Size includes all history
    length(am_save(doc20))
    #> [1] 508

    am_close(doc20)

Mitigation strategies:

1. Commit less frequently for bulk changes
2. Compact history periodically (future feature)
3. Use appropriate granularity (don’t make objects too fine-grained)

### Deletions and Concurrent Operations

Deleted items leave tombstones to ensure consistent deletion across replicas. When a delete operation conflicts with an update operation, Automerge has specific resolution rules:

Maps and Lists: When one replica deletes a key/element while another updates it concurrently, the update takes precedence. The updated value is retained in the merged document.

    # Map: Deletion vs concurrent update - update wins
    doc21 <- am_create()
    doc21[["temp"]] <- "value"
    am_commit(doc21)

    doc22 <- am_fork(doc21)

    am_delete(doc21, AM_ROOT, "temp")
    am_commit(doc21)

    doc22[["temp"]] <- "updated"
    am_commit(doc22)

    am_merge(doc21, doc22)
    doc21[["temp"]] # Update takes precedence over delete
    #> [1] "updated"

    am_close(doc21)
    am_close(doc22)

Double deletion: When both replicas delete the same key/element, the key/element is removed from the merged document.

    # List: Delete and insert at same position - both operations apply
    doc23 <- am_create()
    am_put(doc23, AM_ROOT, "items", AM_OBJ_TYPE_LIST)
    items23 <- am_get(doc23, AM_ROOT, "items")
    am_put(doc23, items23, "end", "A")
    am_put(doc23, items23, "end", "B")
    am_put(doc23, items23, "end", "C")
    am_commit(doc23)

    doc24 <- am_fork(doc23)
    items24 <- am_get(doc24, AM_ROOT, "items")

    am_delete(doc23, items23, 2)
    am_insert(doc24, items24, 2, "X")

    am_merge(doc23, doc24)

    for (i in seq_len(am_length(doc23, items23))) {
      print(am_get(doc23, items23, i))
    }
    #> [1] "A"
    #> [1] "X"
    #> [1] "C"

    am_close(doc23)
    am_close(doc24)

## Best Practices

### 1. Choose the Right CRDT Type

- Simple values: Use regular types (strings, numbers, booleans)
- Structured data: Use maps for objects with independent fields
- Ordered collections: Use lists for sequences
- Collaborative text: Use text objects, not strings
- Counters: Use counter CRDT, not integers

### 2. Design for Commutativity

Structure your data so concurrent operations naturally merge well:

    # Good: Independent counters per user
    doc_good <- am_create()
    doc_good[["votes"]] <- list(
      alice = am_counter(0),
      bob = am_counter(0)
    )

    # Better than: Single counter for all votes
    doc_bad <- am_create()
    doc_bad[["total_votes"]] <- am_counter(0) # Loses attribution

    am_close(doc_good)
    am_close(doc_bad)

### 3. Commit at Logical Boundaries

Group related changes in commits:

    doc25 <- am_create()

    # Good: Atomic transaction
    doc25[["user"]] <- list(name = "Alice", age = 30L, city = "Boston")
    am_commit(doc25, "Add user Alice")

    # Bad: Many micro-commits (increases storage)
    doc25[["status"]] <- "active"
    am_commit(doc25, "Set status")
    doc25[["role"]] <- "admin"
    am_commit(doc25, "Set role")

    am_close(doc25)

### 4. Handle Concurrent Conflicts Gracefully

Understand that some conflicts are inevitable:

    doc26 <- am_create()
    doc26[["status"]] <- "draft"
    am_commit(doc26)

    doc27 <- am_fork(doc26)

    doc26[["status"]] <- "published"
    doc27[["status"]] <- "archived"

    am_merge(doc26, doc27)

    # One will win - application should handle both states sensibly
    doc26[["status"]] # Should be prepared for either 'published' or 'archived'
    #> [1] "archived"

    # Use am_equal() to check if two documents have converged
    am_merge(doc27, doc26)
    am_equal(doc26, doc27) # TRUE - both have the same state now
    #> [1] TRUE

    am_close(doc26)
    am_close(doc27)

## Further Reading

- Automerge Documentation
- CRDT Research Papers
- Conflict-Free Replicated Data Types (Paper)

## Next Steps

- Learn about Synchronization Patterns to use CRDTs in practice
- Check the Quick Reference for all available functions