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Give every worker or CI shard in a single Applitools test run the same batch ID. For Playwright, the simplest documented approach is to set APPLITOOLS_BATCH_ID before starting the test command, using a newly generated ID for each distinct run. Separate runs should use different IDs so unrelated results do not get grouped together.
Why parallel Applitools tests split into multiple batches
An Applitools batch groups related test results in a common dashboard container. Parallel Playwright tests run in separate worker processes, which do not share global variables or in-memory objects. If each worker creates a BatchInfo object without an explicit shared ID, each can generate a different ID, and the dashboard may show several batches instead of one.
The batch ID is the grouping mechanism across processes or machines. A batch name can make the resulting group easier for people to identify, but a shared name alone is not a substitute for a shared ID. See Applitools’ parallel Playwright guidance and batch configuration documentation.
Set one batch ID for all workers in a run
- At the start of the intended test run, generate a fresh unique ID. Applitools recommends a unique ID for each batch; a UUID is a practical choice because accidental collisions are very unlikely.
- Make that exact value available to every worker or shard that should contribute to the batch.
- For Playwright, set
APPLITOOLS_BATCH_IDin the environment before invoking the test command. For example, in a POSIX shell:export APPLITOOLS_BATCH_ID="$(node -e 'console.log(require("crypto").randomUUID())')"
npx playwright test
This generates one ID in the parent shell and inherits it in the test process and its workers. In CI, create the value once for the run and distribute it to every participating job; do not generate a separate value independently inside each shard. - Use a different ID for another test run, including a concurrent run. Reusing a static ID risks merging unrelated results.
- Give the batch a useful name if you want the dashboard group to be recognizable. The ID determines grouping; the name is for identification.
Applitools also demonstrates a commit-derived value for a sharded Storybook workflow. That can group the shards in that workflow, but make sure the chosen value distinguishes separate runs that should remain separate. See the Storybook scaling guide.
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Choose between environment configuration and BatchInfo
| Approach | Where the ID is configured | What to ensure |
|---|---|---|
APPLITOOLS_BATCH_ID |
Process environment or CI configuration | Every worker and shard receives the same value before tests start. Convenient when a run coordinator distributes configuration across processes or machines. |
SDK BatchInfo |
Test code | Assign the same explicit ID in every process before opening tests. Creating a separate object in each worker without a shared ID may still produce different batches. |
Applitools’ batching documentation includes language-specific examples for Java, JavaScript, Python, Ruby, and C#. Check the syntax for the SDK version installed in your project before adapting one. The environment-variable example above is documented for parallel Playwright use; the sources do not establish a universal compatibility matrix for every runner and SDK version.
Troubleshoot batches that still split
- Workers show different IDs: inspect the effective
APPLITOOLS_BATCH_IDin each worker or shard. Generate it once per run, not once per worker. - The variable exists locally but not in CI containers: confirm the CI system explicitly forwards the run-level variable into each job or container, then verify it is present when the test process starts.
- Unrelated runs appear together: check for a hard-coded or reused ID. Generate a fresh ID for each intended run, including concurrent runs.
- You configure
BatchInfoin code: verify every process assigns the same ID before opening tests. A per-worker object alone does not guarantee shared grouping. - All workers appear configured consistently, but results still split: inspect the effective environment and the runner/SDK configuration in each process. Exact APIs and environment handling can differ by version, so consult the documentation for the versions in use.
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