Fault-tolerant and Transactional Stateful Serverless Workflows
Last updated
Last updated
This paper introduces Beldi, which guarantees exactly-once semantics to workflows in presence of worker crash failures.
Stateful serverless functions (SSFs) usually keep their state in low-latency NoSQL databases (e,g., DynamoDB and Bigtable). However, if a worker hangs or crashes, existing serverless platforms either 1) do nothing, leaving the workflow incomplete, or 2) restart the function on a different worker, potentially violating the exactly-once semantics. As a result, serverless providers recommend that developers write SSFs that are idempotent to ensure that re-execution is safe.
Beldi executes SSF operations while atomically logging these actions and periodically re-executes SSFs that have not yet finished. The logs prevent duplicating work that has already been done, guaranteeing at-most-once semantics, while the re-execution ensures at-least-once semantics.
Figure 1 depicts Beldi’s high-level architecture. It consists of four components: (1) the Beldi library, which exposes APIs for invocations, database reads/writes, and transactions; (2) a set of database tables that store the SSF’s state, as well as logs of reads, writes, and invocations; (3) an intent collector, which is a serverless function that restarts any instances of the corresponding SSF that have stalled or crashed; and (4) a garbage collector, which is a serverless function that keeps the logs from growing unboundedly.
Each intent (i.e., an arbitrary code snippet) is assigned a unique identifier (intent id), which is used to save its progress to an intent table. Each external operation(e.g., reading a value from storage) in the intent is assigned a monotonically increasing step number. Whenever an intent tries to execute an external operation, the client (1) determines the operation’s step number; (2) performs the operation (e.g., writes to the database); (3) logs the intent id, step number, and the operation’s return value (if any) into a separate database table called the operation log.
To ensure at-most-once semantics, Beldi performs actions (2) and (3) atomically* by using a technique called distributed atomic affinity logging (DAAL), which collocates log entries for an item in the same atomicity scope with the item's data. For example, in a storage system where operations are atomic at the row level, Beldi would store the item’s value and its log entries in different columns of the same row.
*if the worker crashed between (2) and (3), at-most-once semantics will be violated.
