Conductor Data Source
You set up a conductor data source by linking your workflow engine to a backing store. This store holds workflow state, task queues, and execution logs for every run. Engineering teams often use one shared database user for both the engine and human operators. When teams share broad database roles, any user or service can read or change live state. How a conductor data source works in practice A workflow engine needs a fast store to track every running job and task queue. When a workflow star
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You set up a conductor data source by linking your workflow engine to a backing store. This store holds workflow state, task queues, and execution logs for every run. Engineering teams often use one shared database user for both the engine and human operators. When teams share broad database roles, any user or service can read or change live state.
How a conductor data source works in practice
A workflow engine needs a fast store to track every running job and task queue. When a workflow starts, the engine writes the initial state to the conductor data source. Worker nodes poll the queue, pick up tasks, and write their results back to the store. This architecture keeps the engine stateless so you can scale worker nodes up or down. You configure the engine with a database host, port, and a role that has write rights. The engine uses a pool of sessions to read and update state records all day. If you use PostgreSQL or MySQL, the engine stores metadata and execution history in tables. Some teams also use an index store to search workflow runs by status or correlation ID. Operators and developers also query this same store when they need to check failed runs. They log in to inspect payloads, fix stuck workflows, or run reports on task queues. In many setups, operators use the same database role that the workflow engine uses.
What native database roles solve and what they miss
Native database roles let you control which users and apps can log in to the store. You can create one role for the workflow engine and another role for human operators. You can also grant read-only rights to developers who only need to check workflow logs. This basic setup stops unauthorized users from logging in to the database from unknown hosts. Still, native roles only check who logs in at the start of a database session. They do not inspect what individual queries or updates do while the session is active. Workflow payloads often carry sensitive customer data like emails, phone numbers, and card numbers. Anyone with read rights on the payload table can query and export those raw fields. Also, an operator with write rights can run a broad update without a peer check. If a query lacks a filter, it can corrupt the state of thousands of workflows. Shared service accounts also hide which specific engineer ran an ad-hoc query during an incident. Audit logs in the database only show the shared role name, not the real user identity.
Scoping workflow store permissions for teams and workers
To reduce risk, separate the permissions of automated workers from the permissions of human engineers. Give the workflow engine role rights only to the specific tables it needs to run. Do not let human engineers log in with the engine service account during daily work. Instead, map human database sessions to individual user identities from your central identity provider. Limit write sessions for humans so they require approval before someone changes live workflow state. When developers query workflow payloads to fix a bug, rewrite sensitive fields in the results. Masking emails, phone numbers, and card numbers in query results keeps customer data safe during checks. Log every query with the individual user identity, the timestamp, and the exact statement run. This approach gives your team clear audit trails and safer day-to-day workflow operations.
Snyk Open Source: architecture patterns and best practices
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Frequently asked questions
What does a conductor data source store?
It stores workflow definitions, task definitions, active workflow state, task queues, and execution payload logs. The workflow engine reads and updates these records at every step of a job.
Why should teams separate engine roles from human operator roles?
The workflow engine needs continuous write rights, while humans only need occasional read or fix rights. Separating them stops engineers from making unapproved bulk changes and keeps audit logs clear.
How can we protect sensitive data inside workflow task payloads?
Task inputs and outputs often hold customer emails, phone numbers, or card numbers in plain text. Applying dynamic data masking rewrites those matched fields in query results when engineers inspect logs.