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new edb1df7171 [DOCS] Minor edits in the doc when mentioning
HoodieDeltaStreamer (#6057)
edb1df7171 is described below
commit edb1df7171a46e4b0c6bf5cf75811a3ae3e482eb
Author: Brandon <[email protected]>
AuthorDate: Thu Jul 7 14:06:36 2022 +0800
[DOCS] Minor edits in the doc when mentioning HoodieDeltaStreamer (#6057)
Using inline code style when mentioning HoodieDeltaStreamer to make the doc
consistent
---
website/docs/hoodie_deltastreamer.md | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/website/docs/hoodie_deltastreamer.md
b/website/docs/hoodie_deltastreamer.md
index 938127da31..607b6a989b 100644
--- a/website/docs/hoodie_deltastreamer.md
+++ b/website/docs/hoodie_deltastreamer.md
@@ -224,7 +224,7 @@ A deltastreamer job can then be triggered as follows:
Read more in depth about concurrency control in the [concurrency control
concepts](/docs/concurrency_control) section
## Checkpointing
-HoodieDeltaStreamer uses checkpoints to keep track of what data has been read
already so it can resume without needing to reprocess all data.
+`HoodieDeltaStreamer` uses checkpoints to keep track of what data has been
read already so it can resume without needing to reprocess all data.
When using a Kafka source, the checkpoint is the [Kafka
Offset](https://cwiki.apache.org/confluence/display/KAFKA/Offset+Management)
When using a DFS source, the checkpoint is the 'last modified' timestamp of
the latest file read.
Checkpoints are saved in the .hoodie commit file as
`deltastreamer.checkpoint.key`.
@@ -316,7 +316,7 @@ For DFS sources the following behaviors are expected:
- `HoodieDeltaStreamer` reads the files under the source base path
(`hoodie.deltastreamer.source.dfs.root`) directly, and it won't use the
partition paths under this base path as fields of the dataset. Detailed
examples can be found [here](https://github.com/apache/hudi/issues/5485).
### Kafka
-Hudi can read directly from Kafka clusters. See more details on
HoodieDeltaStreamer to learn how to setup streaming
+Hudi can read directly from Kafka clusters. See more details on
`HoodieDeltaStreamer` to learn how to setup streaming
ingestion with exactly once semantics, checkpointing, and plugin
transformations. The following formats are supported
when reading data from Kafka:
@@ -335,7 +335,7 @@ to trigger/processing of new or changed data as soon as it
is available on S3.
3. Find the queue URL and Region to set these configurations:
1.
hoodie.deltastreamer.s3.source.queue.url=https://sqs.us-west-2.amazonaws.com/queue/url
2. hoodie.deltastreamer.s3.source.queue.region=us-west-2
-4. start the S3EventsSource and S3EventsHoodieIncrSource using the
HoodieDeltaStreamer utility as shown in sample commands below:
+4. start the S3EventsSource and S3EventsHoodieIncrSource using the
`HoodieDeltaStreamer` utility as shown in sample commands below:
Insert code sample from this blog:
https://hudi.apache.org/blog/2021/08/23/s3-events-source/#configuration-and-setup
@@ -524,7 +524,7 @@ There are 3 use cases for incremental query:
| `read.end-commit` | `false` | the latest commit | -- |
## Kafka Connect Sink
-If you want to perform streaming ingestion into Hudi format similar to
HoodieDeltaStreamer, but you don't want to depend on Spark,
+If you want to perform streaming ingestion into Hudi format similar to
`HoodieDeltaStreamer`, but you don't want to depend on Spark,
try out the new experimental release of Hudi Kafka Connect Sink. Read the
[ReadMe](https://github.com/apache/hudi/tree/master/hudi-kafka-connect)
for full documentation.
