docs(spark): refresh the user guide against the shipped connector (#9607)

The user guide still named `0.78.0`, seven releases behind the `0.85.0`
on Maven Central, so copying the `--jars` URL or the dependency snippet
pulled a stale JAR. `java/vortex-spark/README.md` had drifted too.

Three things the page omits although they ship: `MapType` (covered by
`testWriteAndReadMapColumns`), filter pushdown
(`SupportsPushDownV2Filters` since #7785), and Azure paths
(`fs.azure.account.key*` is forwarded from the Hadoop config).

## Tests

`make -C docs html` succeeds under the repo's `--fail-on-warning`, same
warning count as develop; the documented 2.13 URL returns HTTP 200.

## AI assistance

Written with agentic AI assistance; I verified each claim against the
code it describes.

Signed-off-by: jackylee <qcsd2011@gmail.com>

Author: jackylee-ch

docs/user-guide/spark.md

@@ -6,7 +6,7 @@ connector is published to Maven Central in two flavors:
 - `dev.vortex:vortex-spark_2.13` for Spark 4.x (Scala 2.13)
 - `dev.vortex:vortex-spark_2.12` for Spark 3.5.x (Scala 2.12)
 
-Use the `all` classifier JAR (e.g. `vortex-spark_2.13-0.78.0-all.jar`). It is self-contained:
+Use the `all` classifier JAR (e.g. `vortex-spark_2.13-0.85.0-all.jar`). It is self-contained:
 it bundles the Vortex JNI bindings, native libraries for Linux (x86_64 and aarch64) and macOS
 (aarch64), and relocates its Arrow, Guava, and Jackson dependencies to avoid classpath
 conflicts with Spark. The thin (unclassified) JAR does not work on its own because it
@@ -18,21 +18,21 @@ For `spark-shell`, `spark-submit`, or `pyspark`, pass the `all` JAR with `--jars
 accepts either a local path or a URL, so you can point directly at Maven Central:
 
 ```shell
-spark-shell --jars https://repo1.maven.org/maven2/dev/vortex/vortex-spark_2.13/0.78.0/vortex-spark_2.13-0.78.0-all.jar
+spark-shell --jars https://repo1.maven.org/maven2/dev/vortex/vortex-spark_2.13/0.85.0/vortex-spark_2.13-0.85.0-all.jar
 ```
 
 Or equivalently when building a session programmatically, e.g. in PySpark:
 
 ```python
 spark = (
     SparkSession.builder
-    .config("spark.jars", "/path/to/vortex-spark_2.13-0.78.0-all.jar")
+    .config("spark.jars", "/path/to/vortex-spark_2.13-0.85.0-all.jar")
     .getOrCreate()
 )
 ```
 
 ```{note}
-`--packages dev.vortex:vortex-spark_2.13:0.78.0` does not work: `--packages` cannot select
+`--packages dev.vortex:vortex-spark_2.13:0.85.0` does not work: `--packages` cannot select
 the `all` classifier and resolves the thin JAR, which fails at runtime with
 `NoClassDefFoundError: dev/vortex/relocated/...`.
 ```
@@ -47,7 +47,7 @@ To depend on the connector from a JVM project, add the `all` classifier to the d
 Gradle (Kotlin):
 
 ```kotlin
-implementation("dev.vortex:vortex-spark_2.13:0.78.0:all")
+implementation("dev.vortex:vortex-spark_2.13:0.85.0:all")
 ```
 
 Maven:
@@ -56,7 +56,7 @@ Maven:
 <dependency>
     <groupId>dev.vortex</groupId>
     <artifactId>vortex-spark_2.13</artifactId>
-    <version>0.78.0</version>
+    <version>0.85.0</version>
     <classifier>all</classifier>
 </dependency>
 ```
@@ -79,6 +79,11 @@ partition per file.
 
 Column pruning is pushed down — only the columns referenced by the query are read from the file.
 
+Filter pushdown is also supported. Comparisons (`=`, `<>`, `>`, `>=`, `<`, `<=`), `IS NULL`,
+`IS NOT NULL`, `IN`, the string predicates `STARTS_WITH`, `ENDS_WITH` and `CONTAINS`, and a bare
+boolean column are evaluated during the native scan. Anything else, including predicates on
+partition columns, is returned to Spark for post-scan evaluation.
+
 ## Writing Vortex Files
 
 ```java
@@ -187,6 +192,7 @@ INSERT INTO vortex.`/path/to/data` VALUES (1, 'Alice', 20);
 | `TimestampNTZType` | Timestamp (microseconds, no timezone)  |
 | `ArrayType`        | List                                   |
 | `StructType`       | Struct                                 |
+| `MapType`          | Map                                    |
 
 ## S3 Support
 
@@ -198,3 +204,17 @@ Dataset<Row> df = spark.read()
     .option("path", "s3://bucket/path/to/data")
     .load();
 ```
+
+## Azure Support
+
+Azure Data Lake Storage paths work the same way:
+
+```java
+Dataset<Row> df = spark.read()
+    .format("vortex")
+    .option("path", "abfss://container@account.dfs.core.windows.net/path/to/data")
+    .load();
+```
+
+Account keys are read from the Hadoop configuration: any `fs.azure.account.key*` entry is
+forwarded to the native reader as the storage account key.

java/vortex-spark/README.md

@@ -11,7 +11,7 @@ Two flavors are published to Maven Central:
 | `dev.vortex:vortex-spark_2.13` | Spark 4.x | 2.13  |
 | `dev.vortex:vortex-spark_2.12` | Spark 3.5.x | 2.12 |
 
-Use the `all` classifier JAR (e.g. `vortex-spark_2.13-0.83.0-all.jar`). It is self-contained:
+Use the `all` classifier JAR (e.g. `vortex-spark_2.13-0.85.0-all.jar`). It is self-contained:
 it bundles the Vortex JNI bindings, native libraries for Linux (x86_64 and aarch64) and macOS
 (aarch64), and relocates its Arrow, Guava, and Jackson dependencies to avoid classpath
 conflicts with Spark. The thin (unclassified) JAR does not work on its own because it
@@ -23,28 +23,28 @@ Pass the `all` JAR to `spark-shell`, `spark-submit`, or `pyspark` with `--jars`.
 either a local path or a URL, so you can point directly at Maven Central:
 
 ```shell
-spark-shell --jars https://repo1.maven.org/maven2/dev/vortex/vortex-spark_2.13/0.83.0/vortex-spark_2.13-0.83.0-all.jar
+spark-shell --jars https://repo1.maven.org/maven2/dev/vortex/vortex-spark_2.13/0.85.0/vortex-spark_2.13-0.85.0-all.jar
 ```
 
 Or configure it on the session builder, e.g. in PySpark:
 
 ```python
 spark = (
     SparkSession.builder
-    .config("spark.jars", "/path/to/vortex-spark_2.13-0.83.0-all.jar")
+    .config("spark.jars", "/path/to/vortex-spark_2.13-0.85.0-all.jar")
     .getOrCreate()
 )
 ```
 
-Note that `--packages dev.vortex:vortex-spark_2.13:0.83.0` does not work: `--packages` cannot
+Note that `--packages dev.vortex:vortex-spark_2.13:0.85.0` does not work: `--packages` cannot
 select the `all` classifier and resolves the thin JAR, which fails at runtime with
 `NoClassDefFoundError: dev/vortex/relocated/...`.
 
 To depend on the connector from a JVM project instead, add the `all` classifier to the
 dependency:
 
 ```kotlin
-implementation("dev.vortex:vortex-spark_2.13:0.83.0:all")
+implementation("dev.vortex:vortex-spark_2.13:0.85.0:all")
 ```
 
 ## Usage