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The following commit(s) were added to refs/heads/branch-0.8 by this push:
new ba721cddd9 [MINOR] fix(docs): Fix several document errors (#6263)
ba721cddd9 is described below
commit ba721cddd9707d4d3a2f88a0a0aec5fe9c237fe0
Author: github-actions[bot]
<41898282+github-actions[bot]@users.noreply.github.com>
AuthorDate: Wed Jan 15 23:07:15 2025 +0800
[MINOR] fix(docs): Fix several document errors (#6263)
### What changes were proposed in this pull request?
Fix several errors in the document about hadoop-catalog and hive-catalog
### Why are the changes needed?
Improving the user experience.
### Does this PR introduce _any_ user-facing change?
N/A.
### How was this patch tested?
N/A.
Co-authored-by: Qi Yu <[email protected]>
---
docs/hadoop-catalog-with-gcs.md | 2 +-
docs/hadoop-catalog-with-oss.md | 5 ++---
docs/hive-catalog-with-cloud-storage.md | 11 ++++++++---
3 files changed, 11 insertions(+), 7 deletions(-)
diff --git a/docs/hadoop-catalog-with-gcs.md b/docs/hadoop-catalog-with-gcs.md
index 5422047efd..29465c2549 100644
--- a/docs/hadoop-catalog-with-gcs.md
+++ b/docs/hadoop-catalog-with-gcs.md
@@ -47,7 +47,7 @@ Refer to [Fileset
configurations](./hadoop-catalog.md#fileset-properties) for mo
This section will show you how to use the Hadoop catalog with GCS in
Gravitino, including detailed examples.
-### Create a Hadoop catalog with GCS
+### Step1: Create a Hadoop catalog with GCS
First, you need to create a Hadoop catalog with GCS. The following example
shows how to create a Hadoop catalog with GCS:
diff --git a/docs/hadoop-catalog-with-oss.md b/docs/hadoop-catalog-with-oss.md
index b9ef5f44e2..f330f7ede9 100644
--- a/docs/hadoop-catalog-with-oss.md
+++ b/docs/hadoop-catalog-with-oss.md
@@ -123,7 +123,7 @@ oss_catalog =
gravitino_client.create_catalog(name="test_catalog",
</TabItem>
</Tabs>
-Step 2: Create a Schema
+### Step 2: Create a Schema
Once the Hadoop catalog with OSS is created, you can create a schema inside
that catalog. Below are examples of how to do this:
@@ -174,11 +174,10 @@ catalog.as_schemas().create_schema(name="test_schema",
</Tabs>
-### Create a fileset
+### Step3: Create a fileset
Now that the schema is created, you can create a fileset inside it. Here’s how:
-
<Tabs groupId="language" queryString>
<TabItem value="shell" label="Shell">
diff --git a/docs/hive-catalog-with-cloud-storage.md
b/docs/hive-catalog-with-cloud-storage.md
index 49a018907b..b1403ba5e1 100644
--- a/docs/hive-catalog-with-cloud-storage.md
+++ b/docs/hive-catalog-with-cloud-storage.md
@@ -1,8 +1,8 @@
---
-title: "Hive catalog with s3 and adls"
+title: "Hive catalog with S3, ADLS and GCS"
slug: /hive-catalog
date: 2024-9-24
-keyword: Hive catalog cloud storage S3 ADLS
+keyword: Hive catalog cloud storage S3 ADLS GCS
license: "This software is licensed under the Apache License version 2."
---
@@ -84,8 +84,13 @@ cp ${HADOOP_HOME}/share/hadoop/tools/lib/*aws*
${HIVE_HOME}/lib
# For Azure Blob Storage(ADLS)
cp ${HADOOP_HOME}/share/hadoop/tools/lib/*azure* ${HIVE_HOME}/lib
+
+# For Google Cloud Storage(GCS)
+cp gcs-connector-hadoop3-2.2.22-shaded.jar ${HIVE_HOME}/lib
```
+[`gcs-connector-hadoop3-2.2.22-shaded.jar`](https://github.com/GoogleCloudDataproc/hadoop-connectors/releases/download/v2.2.22/gcs-connector-hadoop2-2.2.22-shaded.jar)
is the bundle jar that contains Hadoop GCS connector, you need to choose the
corresponding gcs connector jar for the version of Hadoop you are using.
+
Alternatively, you can download the required JARs from the Maven repository
and place them in the Hive classpath. It is crucial to verify that the JARs are
compatible with the version of Hadoop you are using to avoid any compatibility
issue.
### Restart Hive metastore
@@ -265,7 +270,7 @@ To access S3-stored tables using Spark, you need to
configure the SparkSession a
sparkSession.sql("...");
```
-:::Note
+:::note
Please download [Hadoop AWS
jar](https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-aws), [aws
java sdk
jar](https://mvnrepository.com/artifact/com.amazonaws/aws-java-sdk-bundle) and
place them in the classpath of the Spark. If the JARs are missing, Spark will
not be able to access the S3 storage.
Azure Blob Storage(ADLS) requires the [Hadoop Azure
jar](https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-azure), [Azure
cloud sdk jar](https://mvnrepository.com/artifact/com.azure/azure-storage-blob)
to be placed in the classpath of the Spark.
for Google Cloud Storage(GCS), you need to download the [Hadoop GCS
jar](https://github.com/GoogleCloudDataproc/hadoop-connectors/releases) and
place it in the classpath of the Spark.
