feat(pyspark): add official support and ci testing with spark connect

.env

@@ -5,3 +5,4 @@ PGPASSWORD="postgres"
 MYSQL_PWD="ibis"
 MSSQL_SA_PASSWORD="1bis_Testing!"
 DRUID_URL="druid://localhost:8082/druid/v2/sql"
+SPARK_CONFIG=./docker/spark-connect/conf.properties

.github/workflows/ibis-backends.yml

@@ -442,13 +442,9 @@ jobs:
       - name: download backend data
         run: just download-data
 
-      - name: show docker compose version
-        if: matrix.backend.services != null
-        run: docker compose version
-
       - name: start services
         if: matrix.backend.services != null
-        run: docker compose up --wait ${{ join(matrix.backend.services, ' ') }}
+        run: just up ${{ join(matrix.backend.services, ' ') }}
 
       - name: install python
         uses: actions/setup-python@v5
@@ -600,7 +596,7 @@ jobs:
 
       - name: start services
         if: matrix.backend.services != null
-        run: docker compose up --wait ${{ join(matrix.backend.services, ' ') }}
+        run: just up ${{ join(matrix.backend.services, ' ') }}
 
       - name: install python
         uses: actions/setup-python@v5
@@ -653,7 +649,7 @@ jobs:
         run: docker compose logs
 
   test_pyspark:
-    name: PySpark ${{ matrix.pyspark-minor-version }} ubuntu-latest python-${{ matrix.python-version }}
+    name: PySpark ${{ matrix.tag }} ${{ matrix.pyspark-minor-version }} ubuntu-latest python-${{ matrix.python-version }}
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
@@ -665,19 +661,29 @@ jobs:
             deps:
               - "'pandas@<2'"
               - "'numpy@<1.24'"
+            tag: local
           - python-version: "3.11"
             pyspark-version: "3.5.2"
             pyspark-minor-version: "3.5"
             deps:
               - "'pandas@>2'"
               - "'numpy@>1.24'"
+            tag: local
           - python-version: "3.12"
             pyspark-version: "3.5.2"
             pyspark-minor-version: "3.5"
             deps:
               - "'pandas@>2'"
               - "'numpy@>1.24'"
               - setuptools
+            tag: local
+          - python-version: "3.12"
+            pyspark-version: "3.5.2"
+            pyspark-minor-version: "3.5"
+            deps:
+              - setuptools
+            tag: remote
+            SPARK_REMOTE: "sc://localhost:15002"
     steps:
       - name: checkout
         uses: actions/checkout@v4
@@ -691,6 +697,10 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
+      - name: start services
+        if: matrix.tag == 'remote'
+        run: just up spark-connect
+
       - name: download backend data
         run: just download-data
 
@@ -730,7 +740,14 @@ jobs:
         shell: bash
         run: just download-iceberg-jar ${{ matrix.pyspark-minor-version }}
 
-      - name: run tests
+      - name: run spark connect tests
+        if: matrix.tag == 'remote'
+        run: just ci-check -m pyspark
+        env:
+          SPARK_REMOTE: ${{ matrix.SPARK_REMOTE }}
+
+      - name: run spark tests
+        if: matrix.tag == 'local'
         run: just ci-check -m pyspark
 
       - name: check that no untracked files were produced

compose.yaml

@@ -589,6 +589,24 @@ services:
     networks:
       - risingwave
 
+  spark-connect:
+    image: bitnami/spark:3.5.2
+    ports:
+      - 15002:15002
+    command: /opt/bitnami/spark/sbin/start-connect-server.sh --name ibis_testing --packages org.apache.spark:spark-connect_2.12:3.5.2,org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.5.2
+    healthcheck:
+      test:
+        - CMD-SHELL
+        - bash -c 'printf \"GET / HTTP/1.1\n\n\" > /dev/tcp/127.0.0.1/15002; exit $$?;'
+      interval: 5s
+      retries: 6
+    volumes:
+      - spark-connect:/data
+      - $PWD/docker/spark-connect/conf.properties:/opt/bitnami/spark/conf/spark-defaults.conf:ro
+      # - $PWD/docker/spark-connect/log4j2.properties:/opt/bitnami/spark/conf/log4j2.properties:ro
+    networks:
+      - spark-connect
+
 networks:
   impala:
     # docker defaults to naming networks "$PROJECT_$NETWORK" but the Java Hive
@@ -606,6 +624,7 @@ networks:
   exasol:
   flink:
   risingwave:
+  spark-connect:
 
 volumes:
   clickhouse:
@@ -617,3 +636,4 @@ volumes:
   exasol:
   impala:
   risingwave:
+  spark-connect:

docker/spark-connect/conf.properties

@@ -0,0 +1,12 @@
+spark.driver.extraJavaOptions=-Duser.timezone=GMT
+spark.executor.extraJavaOptions=-Duser.timezone=GMT
+spark.jars.packages=org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.5.2
+spark.sql.catalog.local.type=hadoop
+spark.sql.catalog.local.warehouse=warehouse
+spark.sql.catalog.local=org.apache.iceberg.spark.SparkCatalog
+spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions
+spark.sql.legacy.timeParserPolicy=LEGACY
+spark.sql.session.timeZone=UTC
+spark.sql.streaming.schemaInference=true
+spark.ui.enabled=false
+spark.ui.showConsoleProgress=false

docker/spark-connect/log4j2.properties

@@ -0,0 +1,68 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Set everything to be logged to the console
+rootLogger.level = error
+rootLogger.appenderRef.stdout.ref = console
+
+# In the pattern layout configuration below, we specify an explicit `%ex` conversion
+# pattern for logging Throwables. If this was omitted, then (by default) Log4J would
+# implicitly add an `%xEx` conversion pattern which logs stacktraces with additional
+# class packaging information. That extra information can sometimes add a substantial
+# performance overhead, so we disable it in our default logging config.
+# For more information, see SPARK-39361.
+appender.console.type = Console
+appender.console.name = console
+appender.console.target = SYSTEM_ERR
+appender.console.layout.type = PatternLayout
+appender.console.layout.pattern = %d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n%ex
+
+# Set the default spark-shell/spark-sql log level to WARN. When running the
+# spark-shell/spark-sql, the log level for these classes is used to overwrite
+# the root logger's log level, so that the user can have different defaults
+# for the shell and regular Spark apps.
+logger.repl.name = org.apache.spark.repl.Main
+logger.repl.level = error
+
+logger.thriftserver.name = org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver
+logger.thriftserver.level = error
+
+# Settings to quiet third party logs that are too verbose
+logger.jetty1.name = org.sparkproject.jetty
+logger.jetty1.level = error
+logger.jetty2.name = org.sparkproject.jetty.util.component.AbstractLifeCycle
+logger.jetty2.level = error
+logger.replexprTyper.name = org.apache.spark.repl.SparkIMain$exprTyper
+logger.replexprTyper.level = error
+logger.replSparkILoopInterpreter.name = org.apache.spark.repl.SparkILoop$SparkILoopInterpreter
+logger.replSparkILoopInterpreter.level = error
+logger.parquet1.name = org.apache.parquet
+logger.parquet1.level = error
+logger.parquet2.name = parquet
+logger.parquet2.level = error
+
+# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
+logger.RetryingHMSHandler.name = org.apache.hadoop.hive.metastore.RetryingHMSHandler
+logger.RetryingHMSHandler.level = fatal
+logger.FunctionRegistry.name = org.apache.hadoop.hive.ql.exec.FunctionRegistry
+logger.FunctionRegistry.level = error
+
+# For deploying Spark ThriftServer
+# SPARK-34128: Suppress undesirable TTransportException warnings involved in THRIFT-4805
+appender.console.filter.1.type = RegexFilter
+appender.console.filter.1.regex = .*Thrift error occurred during processing of message.*
+appender.console.filter.1.onMatch = deny
+appender.console.filter.1.onMismatch = neutral

ibis/backends/pyspark/__init__.py

@@ -31,9 +31,15 @@
 from ibis.util import deprecated
 
 try:
-    from pyspark.errors import AnalysisException, ParseException
+    from pyspark.errors import ParseException
+    from pyspark.errors.exceptions.connect import SparkConnectGrpcException
 except ImportError:
-    from pyspark.sql.utils import AnalysisException, ParseException
+    from pyspark.sql.utils import ParseException
+
+    # Use a dummy class for when spark connect is not available
+    class SparkConnectGrpcException(Exception):
+        pass
+
 
 if TYPE_CHECKING:
     from collections.abc import Mapping, Sequence
@@ -186,13 +192,6 @@ def do_connect(
         # Databricks Serverless compute only supports limited properties
         # and any attempt to set unsupported properties will result in an error.
         # https://docs.databricks.com/en/spark/conf.html
-        try:
-            from pyspark.errors.exceptions.connect import SparkConnectGrpcException
-        except ImportError:
-            # Use a dummy class for when spark connect is not available
-            class SparkConnectGrpcException(Exception):
-                pass
-
         with contextlib.suppress(SparkConnectGrpcException):
             self._session.conf.set("spark.sql.mapKeyDedupPolicy", "LAST_WIN")
 
@@ -456,7 +455,9 @@ def _register_in_memory_table(self, op: ops.InMemoryTable) -> None:
         df.createTempView(op.name)
 
     def _finalize_memtable(self, name: str) -> None:
-        self._session.catalog.dropTempView(name)
+        """No-op, otherwise a deadlock can occur when using Spark Connect."""
+        if isinstance(session := self._session, pyspark.sql.SparkSession):
+            session.catalog.dropTempView(name)
 
     @contextlib.contextmanager
     def _safe_raw_sql(self, query: str) -> Any:
@@ -579,16 +580,20 @@ def get_schema(
 
         table_loc = self._to_sqlglot_table((catalog, database))
         catalog, db = self._to_catalog_db_tuple(table_loc)
+        session = self._session
         with self._active_catalog_database(catalog, db):
             try:
-                df = self._session.table(table_name)
-            except AnalysisException as e:
-                if not self._session.catalog.tableExists(table_name):
+                df = session.table(table_name)
+                # this is intentionally included in the try block because when
+                # using spark connect, the table-not-found exception coming
+                # from the server will *NOT* be raised until the schema
+                # property is accessed
+                struct = PySparkType.to_ibis(df.schema)
+            except Exception as e:
+                if not session.catalog.tableExists(table_name):
                     raise com.TableNotFound(table_name) from e
                 raise
 
-            struct = PySparkType.to_ibis(df.schema)
-
         return sch.Schema(struct)
 
     def create_table(
@@ -752,7 +757,7 @@ def _create_cached_table(self, name, expr):
         query = self.compile(expr)
         t = self._session.sql(query).cache()
         assert t.is_cached
-        t.createOrReplaceTempView(name)
+        t.createTempView(name)
         # store the underlying spark dataframe so we can release memory when
         # asked to, instead of when the session ends
         self._cached_dataframes[name] = t
@@ -761,7 +766,6 @@ def _create_cached_table(self, name, expr):
     def _drop_cached_table(self, name):
         self._session.catalog.dropTempView(name)
         t = self._cached_dataframes.pop(name)
-        assert t.is_cached
         t.unpersist()
         assert not t.is_cached