Last modified: March 23, 2026

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Concurrent Writes

Concurrent writes happen when two or more clients write to the same key in a database at the same time, each unaware of the other's write. In replicated systems, these writes may arrive at different replicas in different orders, causing the replicas to diverge and hold conflicting values. Without a strategy to detect and resolve these conflicts, the system risks permanent inconsistency across its nodes.

Client A writes X=1              Client B writes X=2
  at time T                        at time T
        |                                |
        v                                v
  +------------+                   +------------+
  |  Replica 1 |                   |  Replica 2 |
  |   X = 1    |                   |   X = 2    |
  +------+-----+                   +------+-----+
         |                                |
         |      (replication lag)         |
         v                                v
  +------------+                   +------------+
  |  Replica 1 |                   |  Replica 2 |
  |  X = 1? 2? |                   |  X = 2? 1? |
  +------------+                   +------------+

  Both replicas received both writes but in
  different orders — which value is correct?

• The core problem is that there is no single source of truth when multiple writers operate independently.
• Replicated databases face this challenge because network latency means writes propagate to other nodes at different speeds.
• Without conflict resolution, two replicas can settle on different final values for the same key, violating eventual consistency guarantees.

Detecting Concurrent Writes

• Both multi-leader and leaderless replication systems must detect concurrent writes before they can resolve them.
• Events may arrive in different orders at different nodes, so the system cannot rely on arrival sequence to determine which write came first.
• Two writes are considered concurrent if neither causally depends on the other — that is, neither writer knew about the other's write when it was issued.
• A write A happens-before write B if B was made with knowledge of A's result; in that case the writes are sequential and B simply overwrites A.
• Determining causality requires the system to track metadata about which prior state each write was based on.

Timeline at Node 1                Timeline at Node 2

  t1: Receive Write A               t1: Receive Write B
      (X = "apple")                     (X = "banana")
        |                                 |
  t2: Receive Write B               t2: Receive Write A
      (X = "banana")                     (X = "apple")
        |                                 |
        v                                 v
  Final state: X = "banana"         Final state: X = "apple"
                 ↑                                  ↑
                 |                                  |
              INCONSISTENT — replicas disagree!

• If nodes simply apply writes in the order they arrive, two nodes can end up with permanently different final values.
• The system must use a deterministic strategy to ensure all replicas converge to the same value regardless of arrival order.

Last Write Wins

• The simplest conflict resolution approach is the LWW (last write wins) strategy, where each write is assigned a timestamp and the write with the highest timestamp is kept.
• All conflicting writes are given an arbitrary ordering based on their timestamps, and only the "latest" write survives.
• LWW is easy to implement because every node can independently pick the same winner by comparing timestamps.
• The major downside is durability — writes that arrived "earlier" according to the timestamp are silently discarded, even if they contained important data.
• Timestamps across distributed nodes are subject to clock-skew, meaning a write that actually happened first could receive a later timestamp and incorrectly win.
• Cassandra and DynamoDB use LWW as their default conflict resolution strategy because it trades correctness for simplicity and availability.
• LWW is safe only when keys are written once and never updated, or when losing some writes is acceptable for the application.

Client A: SET X = "apple"  (timestamp: 100)
  Client B: SET X = "banana" (timestamp: 105)

  +---------------------+          +---------------------+
  |     Replica 1       |          |     Replica 2       |
  |                     |          |                     |
  |  Receives:          |          |  Receives:          |
  |   A (ts=100) first  |          |   B (ts=105) first  |
  |   B (ts=105) second |          |   A (ts=100) second |
  |                     |          |                     |
  |  Keeps: B (ts=105)  |          |  Keeps: B (ts=105)  |
  |  X = "banana"  ✔    |          |  X = "banana"  ✔    |
  +---------------------+          +---------------------+

  Both replicas converge on the same value,
  but Client A's write is lost forever.

Immutable Keys

• An alternative approach is to make each key immutable — once a value is written, it can never be overwritten.
• Every write creates a new unique key (for example by appending a UUID), so concurrent writes never conflict because they target different keys.
• This strategy guarantees that no data is lost, since every write is preserved under its own identifier.
• Immutable keys work well for append-only workloads such as event logs, audit trails, and time-series data.
• The trade-off is that applications must handle multiple versions of conceptually related data, often requiring a separate read-time merge step.
• This approach may not suit use cases that require mutable state, such as a user profile that needs to be updated in place.

Version Numbers

• Version numbers provide a way to track the causal history of a key's value, enabling the system to detect concurrent writes precisely.
• The server maintains a counter for each key that is incremented every time the key is written.
• When a client reads a key, the server returns the current value along with its version number.
• When a client writes, it sends the version number it last observed — this tells the server which state the client's write was based on.
• If the client's version matches the server's current version, the write is a simple sequential update and the server increments the version.
• If the client sends a version that is older than the current version, the server knows a concurrent write has occurred.
• When a concurrent write is detected, the server keeps both values (called siblings) and returns them on the next read, letting the application decide how to merge.
• If the client writes without any prior version (version null), the server treats the write as concurrent with all existing values.
• The application is responsible for merging sibling values — it can do so automatically (for example, taking the union of items in a shopping cart) or by prompting the user to choose.

Server (key: "cart")                Client A                  Client B
  ========================            ========                  ========

  v1: cart = {milk}
        |
        +--- read ----------------------> gets v1, {milk}
        |
        +--- read ----------------------------------------------------> gets v1, {milk}
        |
        +<-- write(v1, {milk, eggs}) ---- Client A adds eggs
        |
  v2: cart = {milk, eggs}
        |
        +<-- write(v1, {milk, bread}) --------------------------------- Client B adds bread
        |                                                                (based on stale v1!)
  v3: cart = [{milk, eggs},
              {milk, bread}]          <-- server keeps BOTH as siblings
        |
        +--- read ----------------------> gets v3, [{milk, eggs}, {milk, bread}]
        |
        +<-- write(v3, {milk, eggs, bread})  Client A merges siblings
        |
  v4: cart = {milk, eggs, bread}      <-- conflict resolved, no data lost

• This approach ensures no writes are silently discarded, unlike last-write-wins.
• The cost is additional complexity in the application layer, which must implement merge logic for sibling values.

Version Vectors

• When data is replicated across multiple nodes, a single version number is not enough — each replica may accept writes independently.
• A version vector extends the version number concept by maintaining a separate counter for every replica.
• The version vector for a key is a collection of (replica, version) pairs — for example {A:3, B:2, C:5} means replica A has seen 3 writes, B has seen 2, and C has seen 5.
• When a client reads from a replica, it receives the key's current version vector along with the value.
• When a client writes, it sends back the version vector it observed, allowing the receiving replica to compare vectors and detect concurrency.
• Vector A dominates vector B if every component in A is greater than or equal to the corresponding component in B — this means A causally follows B.
• If neither vector dominates the other, the writes are concurrent and the system must keep both values as siblings.
• Riak used version vectors (which it called dotted version vectors) to track causality across its distributed vnodes.

Replica A                    Replica B                    Replica C
  =========                    =========                    =========

  Write X = 1
  VA = {A:1}
       |
       +--- replicate -------> VB = {A:1}
       |                            |
       |                       Write X = 2
       |                       VB = {A:1, B:1}
       |                            |
       |                            +--- replicate -------> VC = {A:1, B:1}
       |                                                         |
  Write X = 3                                               Write X = 4
  VA = {A:2}                                                VC = {A:1, B:1, C:1}
       |                                                         |
       +--- compare with C's vector --------------------------->|
       |                                                         |
  {A:2} vs {A:1, B:1, C:1}                                      |
  Neither dominates the other → writes are CONCURRENT            |
  System keeps both values as siblings                           |

• Version vectors allow the system to precisely distinguish between sequential writes (which can be safely overwritten) and concurrent writes (which must be preserved as siblings).
• The size of the vector grows with the number of replicas, not the number of writes, so it remains compact in typical deployments.

Conflict Resolution Strategies Compared

• Choosing the right strategy depends on whether the application can tolerate lost writes and how much complexity it can absorb in the merge layer.
• Systems that prioritize availability (AP in the CAP theorem) often use LWW for simplicity, while systems that require strong consistency use version vectors.
• Many production systems combine strategies — for example, using LWW for non-critical metadata and version vectors for business-critical data.