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new 5cf4248 Docs: Fixes broken links to old spark doc page (#2801)
5cf4248 is described below
commit 5cf4248ecc56d531e3b220c7b3631fef22807bf0
Author: Russell Spitzer <[email protected]>
AuthorDate: Fri Jul 9 13:50:17 2021 -0500
Docs: Fixes broken links to old spark doc page (#2801)
---
site/docs/aws.md | 2 +-
site/docs/evolution.md | 4 ++--
site/docs/spark-structured-streaming.md | 4 ++--
3 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/site/docs/aws.md b/site/docs/aws.md
index ca91ddd..96207a7 100644
--- a/site/docs/aws.md
+++ b/site/docs/aws.md
@@ -163,7 +163,7 @@ an Iceberg table is stored as a [Glue
Table](https://docs.aws.amazon.com/glue/la
and every Iceberg table version is stored as a [Glue
TableVersion](https://docs.aws.amazon.com/glue/latest/dg/aws-glue-api-catalog-tables.html#aws-glue-api-catalog-tables-TableVersion).
You can start using Glue catalog by specifying the `catalog-impl` as
`org.apache.iceberg.aws.glue.GlueCatalog`,
just like what is shown in the [enabling AWS
integration](#enabling-aws-integration) section above.
-More details about loading the catalog can be found in individual engine
pages, such as [Spark](../spark/#loading-a-custom-catalog) and
[Flink](../flink/#creating-catalogs-and-using-catalogs).
+More details about loading the catalog can be found in individual engine
pages, such as [Spark](../spark-configuration/#loading-a-custom-catalog) and
[Flink](../flink/#creating-catalogs-and-using-catalogs).
### Glue Catalog ID
There is a unique Glue metastore in each AWS account and each AWS region.
diff --git a/site/docs/evolution.md b/site/docs/evolution.md
index 0b4bec2..624a986 100644
--- a/site/docs/evolution.md
+++ b/site/docs/evolution.md
@@ -74,7 +74,7 @@ sampleTable.updateSpec()
.commit();
```
-Spark supports updating partition spec through its `ALTER TABLE` SQL
statement, see more details in [Spark
SQL](../spark/#alter-table-add-partition-field).
+Spark supports updating partition spec through its `ALTER TABLE` SQL
statement, see more details in [Spark
SQL](../spark-ddl/#alter-table-add-partition-field).
## Sort order evolution
@@ -95,4 +95,4 @@ sampleTable.replaceSortOrder()
.commit();
```
-Spark supports updating sort order through its `ALTER TABLE` SQL statement,
see more details in [Spark SQL](../spark/#alter-table-write-ordered-by).
+Spark supports updating sort order through its `ALTER TABLE` SQL statement,
see more details in [Spark SQL](../spark-ddl/#alter-table-write-ordered-by).
diff --git a/site/docs/spark-structured-streaming.md
b/site/docs/spark-structured-streaming.md
index f4e54bd..b969dcb 100644
--- a/site/docs/spark-structured-streaming.md
+++ b/site/docs/spark-structured-streaming.md
@@ -53,14 +53,14 @@ Iceberg supports `append` and `complete` output modes:
* `append`: appends the rows of every micro-batch to the table
* `complete`: replaces the table contents every micro-batch
-The table should be created in prior to start the streaming query. Refer [SQL
create table](/spark/#create-table)
+The table should be created in prior to start the streaming query. Refer [SQL
create table](/spark-ddl/#create-table)
on Spark page to see how to create the Iceberg table.
### Writing against partitioned table
Iceberg requires the data to be sorted according to the partition spec per
task (Spark partition) in prior to write
against partitioned table. For batch queries you're encouraged to do explicit
sort to fulfill the requirement
-(see [here](/spark/#writing-against-partitioned-table)), but the approach
would bring additional latency as
+(see [here](/spark-writes/#writing-to-partitioned-tables)), but the approach
would bring additional latency as
repartition and sort are considered as heavy operations for streaming
workload. To avoid additional latency, you can
enable fanout writer to eliminate the requirement.
