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new d4bd6e5 ingestion and tutorial doc update (#10202)
d4bd6e5 is described below
commit d4bd6e52070394a651724697ce588c9fe4f81436
Author: mans2singh <[email protected]>
AuthorDate: Tue Jul 21 20:52:23 2020 -0400
ingestion and tutorial doc update (#10202)
---
docs/ingestion/index.md | 2 +-
docs/ingestion/native-batch.md | 2 +-
docs/tutorials/tutorial-batch-hadoop.md | 2 +-
3 files changed, 3 insertions(+), 3 deletions(-)
diff --git a/docs/ingestion/index.md b/docs/ingestion/index.md
index 384b051..d84c0e4 100644
--- a/docs/ingestion/index.md
+++ b/docs/ingestion/index.md
@@ -284,7 +284,7 @@ The following table shows how each ingestion method handles
partitioning:
## Ingestion specs
No matter what ingestion method you use, data is loaded into Druid using
either one-time [tasks](tasks.html) or
-ongoing "supervisors" (which run and supervised a set of tasks over time). In
any case, part of the task or supervisor
+ongoing "supervisors" (which run and supervise a set of tasks over time). In
any case, part of the task or supervisor
definition is an _ingestion spec_.
Ingestion specs consists of three main components:
diff --git a/docs/ingestion/native-batch.md b/docs/ingestion/native-batch.md
index 4dbbca7..f1c22e2 100644
--- a/docs/ingestion/native-batch.md
+++ b/docs/ingestion/native-batch.md
@@ -261,7 +261,7 @@ The three `partitionsSpec` types have different
characteristics.
The recommended use case for each partitionsSpec is:
- If your data has a uniformly distributed column which is frequently used in
your queries,
consider using `single_dim` partitionsSpec to maximize the performance of most
of your queries.
-- If your data doesn't a uniformly distributed column, but is expected to have
a [high rollup ratio](./index.md#maximizing-rollup-ratio)
+- If your data doesn't have a uniformly distributed column, but is expected to
have a [high rollup ratio](./index.md#maximizing-rollup-ratio)
when you roll up with some dimensions, consider using `hashed` partitionsSpec.
It could reduce the size of datasource and query latency by improving data
locality.
- If the above two scenarios are not the case or you don't need to roll up
your datasource,
diff --git a/docs/tutorials/tutorial-batch-hadoop.md
b/docs/tutorials/tutorial-batch-hadoop.md
index 38abbfa..bd02464 100644
--- a/docs/tutorials/tutorial-batch-hadoop.md
+++ b/docs/tutorials/tutorial-batch-hadoop.md
@@ -205,7 +205,7 @@ We've included a sample of Wikipedia edits from September
12, 2015 to get you st
To load this data into Druid, you can submit an *ingestion task* pointing to
the file. We've included
a task that loads the `wikiticker-2015-09-12-sampled.json.gz` file included in
the archive.
-Let's submit the `wikipedia-index-hadoop-.json` task:
+Let's submit the `wikipedia-index-hadoop.json` task:
```bash
bin/post-index-task --file quickstart/tutorial/wikipedia-index-hadoop.json
--url http://localhost:8081
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