/// -> //

src/dataflow/sink/kafka.rs

@@ -16,31 +16,31 @@ use rdkafka::config::ClientConfig;
 use rdkafka::producer::FutureProducer;
 use rdkafka::producer::FutureRecord;
 
-/// TODO@jldlaughlin: Progress tracking for kafka sinks #1442
-///
-/// Right now, every time Materialize crashes and recovers these sinks
-/// will resend each record to Kafka. This is not entirely horrible, but also
-/// obviously not ideal! But until we have a more concrete idea of what
-/// people will require from Kafka sinks, we're punting on implementing this.
-///
-/// For posterity, here are some of the options we discussed to
-/// implement this:
-///      - Use differential's Consolidate operator on the batches we are
-///        iterating through. Track our progress in sending batches to Kafka
-///        using the "watermarks" from the consolidated batch (inclusive on the
-///        lower bound, exclusive on the upper bound). Store these timestamps
-///        persistently (in mzdata/catalog) in order to either:
-///            - Resend everything including and after the last successfully sent batch.
-///              This assumes the Kafka topic we are sending to handles duplicates.
-///            - First, send a negative diff of each record in the last successful
-///              batch. Then, resend everything after.
-///
-///     - Append something like a "Materialize start up timestamp" to the
-///       end of the Kafka topic name. This accepts resending all of the data,
-///       but will not duplicate data in a single topic.
-///            - NB: This, like other decisions we've made, assumes that
-///              the user has configured their Kafka instance to automatically
-///              create new topics.
+// TODO@jldlaughlin: Progress tracking for kafka sinks #1442
+//
+// Right now, every time Materialize crashes and recovers these sinks
+// will resend each record to Kafka. This is not entirely horrible, but also
+// obviously not ideal! But until we have a more concrete idea of what
+// people will require from Kafka sinks, we're punting on implementing this.
+//
+// For posterity, here are some of the options we discussed to
+// implement this:
+//      - Use differential's Consolidate operator on the batches we are
+//        iterating through. Track our progress in sending batches to Kafka
+//        using the "watermarks" from the consolidated batch (inclusive on the
+//        lower bound, exclusive on the upper bound). Store these timestamps
+//        persistently (in mzdata/catalog) in order to either:
+//            - Resend everything including and after the last successfully sent batch.
+//              This assumes the Kafka topic we are sending to handles duplicates.
+//            - First, send a negative diff of each record in the last successful
+//              batch. Then, resend everything after.
+//
+//     - Append something like a "Materialize start up timestamp" to the
+//       end of the Kafka topic name. This accepts resending all of the data,
+//       but will not duplicate data in a single topic.
+//            - NB: This, like other decisions we've made, assumes that
+//              the user has configured their Kafka instance to automatically
+//              create new topics.
 pub fn kafka<G>(
     stream: &Stream<G, (Row, Timestamp, Diff)>,
     id: GlobalId,