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authorAlex Browne <stephenalexbrowne@gmail.com>2019-01-11 05:04:34 +0800
committerGitHub <noreply@github.com>2019-01-11 05:04:34 +0800
commit53fc860d6190c19f13c451eb89dd5c0c1cdcd1f4 (patch)
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Add new deployment related instructions to pipeline README (#1499)
* Add new deployment related instructions to pipeline README * Apply prettier * Respond to PR feedback
Diffstat (limited to 'packages/pipeline')
-rw-r--r--packages/pipeline/README.md17
1 files changed, 17 insertions, 0 deletions
diff --git a/packages/pipeline/README.md b/packages/pipeline/README.md
index 794488cac..40a64cdd1 100644
--- a/packages/pipeline/README.md
+++ b/packages/pipeline/README.md
@@ -147,6 +147,20 @@ set the`ZEROEX_DATA_PIPELINE_DB_URL` environment variable to a valid
starting from that block number.
7. Run the migrations and then run your new script locally and verify it works
as expected.
+8. After all tests pass and you can run the script locally, open a new PR to
+ the monorepo. Don't merge this yet!
+9. If you added any new scripts or dependencies between scripts, you will need
+ to make changes to https://github.com/0xProject/0x-pipeline-orchestration
+ and make a separate PR there. Don't merge this yet!
+10. After your PR passes code review, ask @feuGeneA or @xianny to deploy your
+ changes to the QA environment. Check the [QA Airflow dashboard](http://airflow-qa.0x.org:8080)
+ to make sure everything works correctly in the QA environment.
+11. Merge your PR to 0x-monorepo (and
+ https://github.com/0xProject/0x-pipeline-orchestration if needed). Then ask
+ @feuGeneA or @xianny to deploy to production.
+12. Monitor the [production Airflow dashboard](http://airflow.0x.org:8080) to
+ make sure everything still works.
+13. Celebrate! :tada:
#### Additional guidelines and tips:
@@ -164,3 +178,6 @@ set the`ZEROEX_DATA_PIPELINE_DB_URL` environment variable to a valid
floating point numbers.
* [TypeORM documentation](http://typeorm.io/#/) is pretty robust and can be a
helpful resource.
+* Scripts/parsers should perform minimum data transformation/normalization.
+ The idea here is to have a raw data feed that will be cleaned up and
+ synthesized in a separate step.