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new 64aa093529 spark quickstart, change default lang to python, group code
blocks so that when user switches to Scala all code blocks shown will be scala.
(#7288)
64aa093529 is described below
commit 64aa0935298d8f628793f1368c4de6b53645f57a
Author: kazdy <[email protected]>
AuthorDate: Fri Nov 25 00:27:42 2022 +0100
spark quickstart, change default lang to python, group code blocks so that
when user switches to Scala all code blocks shown will be scala. (#7288)
---
website/docs/quick-start-guide.md | 43 ++++++++++++++++++++++++++-------------
1 file changed, 29 insertions(+), 14 deletions(-)
diff --git a/website/docs/quick-start-guide.md
b/website/docs/quick-start-guide.md
index 64ba8e8360..c227b13a47 100644
--- a/website/docs/quick-start-guide.md
+++ b/website/docs/quick-start-guide.md
@@ -36,7 +36,8 @@ instructions.
:::
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -167,7 +168,8 @@ spark-sql --packages
org.apache.hudi:hudi-spark2.4-bundle_2.11:0.12.1 \
Setup table name, base path and a data generator to generate records for this
guide.
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -214,7 +216,8 @@ can generate sample inserts and updates based on the the
sample trip schema [her
## Create Table
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -403,7 +406,8 @@ To set any custom hudi config(like index type, max parquet
size, etc), see the
## Insert data
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -532,7 +536,8 @@ you can also centrally set them in a configuration file
`hudi-default.conf`.
Load the data files into a DataFrame.
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -594,7 +599,8 @@ Refer to [Table types and
queries](/docs/concepts#table-types--queries) for more
Hudi supports time travel query since 0.9.0. Currently three query time
formats are supported as given below.
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -693,7 +699,8 @@ This is similar to inserting new data. Generate updates to
existing trips using
and write DataFrame into the hudi table.
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -829,7 +836,8 @@ This can be achieved using Hudi's incremental querying and
providing a begin tim
We do not need to specify endTime, if we want all changes after the given
commit (as is the common case).
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -905,8 +913,10 @@ Hudi supports Spark Structured Streaming reads and writes.
Structured Streaming reads are based on Hudi Incremental Query feature,
therefore streaming read can return data for which commits and base files were
not yet removed by the cleaner. You can control commits retention time.
### Streaming Read
+
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -991,7 +1001,8 @@ spark.readStream \
### Streaming Write
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -1092,7 +1103,8 @@ Lets look at how to query data as of a specific time. The
specific time can be r
specific commit time and beginTime to "000" (denoting earliest possible commit
time).
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -1156,7 +1168,8 @@ Apache Hudi supports two types of deletes: <br/>
Soft deletes retain the record key and null out the values for all the other
fields. For example, records with nulls in soft deletes are always persisted in
storage and never removed.<br/><br/>
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', }
@@ -1286,7 +1299,8 @@ spark.sql("select uuid, partitionpath from
hudi_trips_snapshot where rider is no
Hard deletes physically remove any trace of the record from the table. For
example, this deletes records for the HoodieKeys passed in.<br/><br/>
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
@@ -1411,7 +1425,8 @@ updating the target tables). This is because, we are able
to bypass indexing, pr
steps in the upsert write path completely.
<Tabs
-defaultValue="scala"
+groupId="programming-language"
+defaultValue="python"
values={[
{ label: 'Scala', value: 'scala', },
{ label: 'Python', value: 'python', },
