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CXSYNC_PORT

Short Description

CXSYNC_PORT is a parallel exchange synchronization wait.

Detailed Description​

CXSYNC_PORT is a parallel query synchronization wait that occurs when SQL Server is executing a query using multiple worker threads and those workers become unevenly balanced.

In plain English:

  • A parallel query is split into multiple worker threads
  • Some workers finish their assigned portion of the work quickly
  • One or more workers are still processing a disproportionately large share
  • The finished workers wait at a parallel exchange operator for the slower workers to catch up

While waiting, those threads accumulate CXSYNC_PORT wait time.

This wait only occurs in parallel execution plans and is most commonly observed when the workload cannot be evenly distributed across workers.

Typical causes include:

  • Self-joins, especially where a table is joined to itself on low-cardinality columns
  • Data skew, where most rows belong to a small number of key values
  • Range predicates (for example, time-window joins) that cause overlapping row sets
  • Queries using HASH JOIN combined with GROUP BY, where one worker processes a much larger hash bucket than others

A high CXSYNC_PORT wait does not indicate blocking or I/O contention. It indicates that SQL Server’s parallel workers are waiting for synchronization due to workload imbalance.

How to reduce this wait​

Reducing CXSYNC_PORT typically involves improving parallel work distribution rather than eliminating parallelism altogether.

Common mitigation strategies include:

1. Reduce Data Skew​

  • Avoid joining or grouping on very low-cardinality columns (such as status codes or small lookup values) in large parallel queries
  • Where possible, introduce additional join or grouping keys that better distribute rows

2. Rewrite Non-SARGable Predicates​

Expressions such as:

ABS(DATEDIFF(minute, a.col, b.col)) <= 10

Prevent efficient row filtering and often worsen skew.

Rewrite these as range predicates:

a.col BETWEEN DATEADD(minute, -10, b.col)
        AND DATEADD(minute,  10, b.col)

This improves cardinality estimates and parallel balance.

3. Pre-Aggregate or Bucket Data​

Breaking large datasets into time buckets or logical partitions before joining or aggregating can significantly reduce skew and synchronization waits.

Examples include:

  • Time bucketing (e.g. 5-minute or hourly buckets)
  • Pre-aggregating fact data before joining to other large sets

4. Reduce Degree of Parallelism (MAXDOP)​

Counter-intuitively, lowering MAXDOP can reduce CXSYNC_PORT:

  • Fewer workers → less synchronization overhead
  • Often results in better overall query latency

This can be applied:

  • At the query level using OPTION (MAXDOP n)
  • At the database or instance level if the pattern is widespread

5. Evaluate Whether Parallelism Is Appropriate​

Not all queries benefit from parallel execution.

Highly skewed workloads, complex self-joins, or OLTP-style queries may perform better with:

  • Serial execution
  • Lower degrees of parallelism

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