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From hvanhov...@apache.org
Subject [2/2] spark git commit: [SPARK-19944][SQL] Move SQLConf from sql/core to sql/catalyst (branch-2.1)
Date Wed, 15 Mar 2017 12:07:28 GMT
[SPARK-19944][SQL] Move SQLConf from sql/core to sql/catalyst (branch-2.1)

## What changes were proposed in this pull request?
This patch moves SQLConf from sql/core to sql/catalyst. To minimize the changes, the patch used type alias to still keep CatalystConf (as a type alias) and SimpleCatalystConf (as a concrete class that extends SQLConf).

Motivation for the change is that it is pretty weird to have SQLConf only in sql/core and then we have to duplicate config options that impact optimizer/analyzer in sql/catalyst using CatalystConf.

This is a backport into branch-2.1 to minimize merge conflicts.

## How was this patch tested?
N/A

Author: Reynold Xin <rxin@databricks.com>

Closes #17301 from rxin/branch-2.1-conf.


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/80ebca62
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/80ebca62
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/80ebca62

Branch: refs/heads/branch-2.1
Commit: 80ebca62cbdb7d5c8606e95a944164ab1a943694
Parents: a0ce845
Author: Reynold Xin <rxin@databricks.com>
Authored: Wed Mar 15 13:07:20 2017 +0100
Committer: Herman van Hovell <hvanhovell@databricks.com>
Committed: Wed Mar 15 13:07:20 2017 +0100

----------------------------------------------------------------------
 .../spark/sql/catalyst/CatalystConf.scala       |   66 --
 .../spark/sql/catalyst/SimpleCatalystConf.scala |   38 +
 .../org/apache/spark/sql/catalyst/package.scala |    7 +
 .../org/apache/spark/sql/internal/SQLConf.scala |  967 +++++++++++++++++
 .../spark/sql/internal/StaticSQLConf.scala      |   77 ++
 .../org/apache/spark/sql/internal/SQLConf.scala | 1010 ------------------
 6 files changed, 1089 insertions(+), 1076 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/80ebca62/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystConf.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystConf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystConf.scala
deleted file mode 100644
index 75ae588..0000000
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystConf.scala
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * 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.
- */
-
-package org.apache.spark.sql.catalyst
-
-import org.apache.spark.sql.catalyst.analysis._
-
-/**
- * Interface for configuration options used in the catalyst module.
- */
-trait CatalystConf {
-  def caseSensitiveAnalysis: Boolean
-
-  def orderByOrdinal: Boolean
-  def groupByOrdinal: Boolean
-
-  def optimizerMaxIterations: Int
-  def optimizerInSetConversionThreshold: Int
-  def maxCaseBranchesForCodegen: Int
-
-  def runSQLonFile: Boolean
-
-  def warehousePath: String
-
-  /** If true, cartesian products between relations will be allowed for all
-   * join types(inner, (left|right|full) outer).
-   * If false, cartesian products will require explicit CROSS JOIN syntax.
-   */
-  def crossJoinEnabled: Boolean
-
-  /**
-   * Returns the [[Resolver]] for the current configuration, which can be used to determine if two
-   * identifiers are equal.
-   */
-  def resolver: Resolver = {
-    if (caseSensitiveAnalysis) caseSensitiveResolution else caseInsensitiveResolution
-  }
-}
-
-
-/** A CatalystConf that can be used for local testing. */
-case class SimpleCatalystConf(
-    caseSensitiveAnalysis: Boolean,
-    orderByOrdinal: Boolean = true,
-    groupByOrdinal: Boolean = true,
-    optimizerMaxIterations: Int = 100,
-    optimizerInSetConversionThreshold: Int = 10,
-    maxCaseBranchesForCodegen: Int = 20,
-    runSQLonFile: Boolean = true,
-    crossJoinEnabled: Boolean = false,
-    warehousePath: String = "/user/hive/warehouse")
-  extends CatalystConf

http://git-wip-us.apache.org/repos/asf/spark/blob/80ebca62/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SimpleCatalystConf.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SimpleCatalystConf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SimpleCatalystConf.scala
new file mode 100644
index 0000000..ab52a90
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SimpleCatalystConf.scala
@@ -0,0 +1,38 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.sql.catalyst
+
+import org.apache.spark.sql.internal.SQLConf
+
+
+/**
+ * A SQLConf that can be used for local testing. This class is only here to minimize the change
+ * for ticket SPARK-19944 (moves SQLConf from sql/core to sql/catalyst). This class should
+ * eventually be removed (test cases should just create SQLConf and set values appropriately).
+ */
+case class SimpleCatalystConf(
+    override val caseSensitiveAnalysis: Boolean,
+    override val orderByOrdinal: Boolean = true,
+    override val groupByOrdinal: Boolean = true,
+    override val optimizerMaxIterations: Int = 100,
+    override val optimizerInSetConversionThreshold: Int = 10,
+    override val maxCaseBranchesForCodegen: Int = 20,
+    override val runSQLonFile: Boolean = true,
+    override val crossJoinEnabled: Boolean = false,
+    override val warehousePath: String = "/user/hive/warehouse")
+  extends SQLConf

http://git-wip-us.apache.org/repos/asf/spark/blob/80ebca62/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala
index 105cdf5..4af56af 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.internal.SQLConf
+
 /**
  * Catalyst is a library for manipulating relational query plans.  All classes in catalyst are
  * considered an internal API to Spark SQL and are subject to change between minor releases.
@@ -29,4 +31,9 @@ package object catalyst {
    */
   protected[sql] object ScalaReflectionLock
 
+  /**
+   * This class is only here to minimize the change for ticket SPARK-19944
+   * (moves SQLConf from sql/core to sql/catalyst). This class should eventually be removed.
+   */
+  type CatalystConf = SQLConf
 }

http://git-wip-us.apache.org/repos/asf/spark/blob/80ebca62/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
new file mode 100644
index 0000000..ad5b103
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
@@ -0,0 +1,967 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.sql.internal
+
+import java.util.{NoSuchElementException, Properties}
+import java.util.concurrent.TimeUnit
+
+import scala.collection.JavaConverters._
+import scala.collection.immutable
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.internal.config._
+import org.apache.spark.network.util.ByteUnit
+import org.apache.spark.sql.catalyst.analysis.Resolver
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// This file defines the configuration options for Spark SQL.
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
+
+object SQLConf {
+
+  private val sqlConfEntries = java.util.Collections.synchronizedMap(
+    new java.util.HashMap[String, ConfigEntry[_]]())
+
+  private[sql] def register(entry: ConfigEntry[_]): Unit = sqlConfEntries.synchronized {
+    require(!sqlConfEntries.containsKey(entry.key),
+      s"Duplicate SQLConfigEntry. ${entry.key} has been registered")
+    sqlConfEntries.put(entry.key, entry)
+  }
+
+  private[sql] object SQLConfigBuilder {
+
+    def apply(key: String): ConfigBuilder = new ConfigBuilder(key).onCreate(register)
+
+  }
+
+  val OPTIMIZER_MAX_ITERATIONS = SQLConfigBuilder("spark.sql.optimizer.maxIterations")
+    .internal()
+    .doc("The max number of iterations the optimizer and analyzer runs.")
+    .intConf
+    .createWithDefault(100)
+
+  val OPTIMIZER_INSET_CONVERSION_THRESHOLD =
+    SQLConfigBuilder("spark.sql.optimizer.inSetConversionThreshold")
+      .internal()
+      .doc("The threshold of set size for InSet conversion.")
+      .intConf
+      .createWithDefault(10)
+
+  val COMPRESS_CACHED = SQLConfigBuilder("spark.sql.inMemoryColumnarStorage.compressed")
+    .internal()
+    .doc("When set to true Spark SQL will automatically select a compression codec for each " +
+      "column based on statistics of the data.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val COLUMN_BATCH_SIZE = SQLConfigBuilder("spark.sql.inMemoryColumnarStorage.batchSize")
+    .internal()
+    .doc("Controls the size of batches for columnar caching.  Larger batch sizes can improve " +
+      "memory utilization and compression, but risk OOMs when caching data.")
+    .intConf
+    .createWithDefault(10000)
+
+  val IN_MEMORY_PARTITION_PRUNING =
+    SQLConfigBuilder("spark.sql.inMemoryColumnarStorage.partitionPruning")
+      .internal()
+      .doc("When true, enable partition pruning for in-memory columnar tables.")
+      .booleanConf
+      .createWithDefault(true)
+
+  val PREFER_SORTMERGEJOIN = SQLConfigBuilder("spark.sql.join.preferSortMergeJoin")
+    .internal()
+    .doc("When true, prefer sort merge join over shuffle hash join.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val RADIX_SORT_ENABLED = SQLConfigBuilder("spark.sql.sort.enableRadixSort")
+    .internal()
+    .doc("When true, enable use of radix sort when possible. Radix sort is much faster but " +
+      "requires additional memory to be reserved up-front. The memory overhead may be " +
+      "significant when sorting very small rows (up to 50% more in this case).")
+    .booleanConf
+    .createWithDefault(true)
+
+  val AUTO_BROADCASTJOIN_THRESHOLD = SQLConfigBuilder("spark.sql.autoBroadcastJoinThreshold")
+    .doc("Configures the maximum size in bytes for a table that will be broadcast to all worker " +
+      "nodes when performing a join.  By setting this value to -1 broadcasting can be disabled. " +
+      "Note that currently statistics are only supported for Hive Metastore tables where the " +
+      "command <code>ANALYZE TABLE &lt;tableName&gt; COMPUTE STATISTICS noscan</code> has been " +
+      "run, and file-based data source tables where the statistics are computed directly on " +
+      "the files of data.")
+    .longConf
+    .createWithDefault(10L * 1024 * 1024)
+
+  val LIMIT_SCALE_UP_FACTOR = SQLConfigBuilder("spark.sql.limit.scaleUpFactor")
+    .internal()
+    .doc("Minimal increase rate in number of partitions between attempts when executing a take " +
+      "on a query. Higher values lead to more partitions read. Lower values might lead to " +
+      "longer execution times as more jobs will be run")
+    .intConf
+    .createWithDefault(4)
+
+  val ENABLE_FALL_BACK_TO_HDFS_FOR_STATS =
+    SQLConfigBuilder("spark.sql.statistics.fallBackToHdfs")
+    .doc("If the table statistics are not available from table metadata enable fall back to hdfs." +
+      " This is useful in determining if a table is small enough to use auto broadcast joins.")
+    .booleanConf
+    .createWithDefault(false)
+
+  val DEFAULT_SIZE_IN_BYTES = SQLConfigBuilder("spark.sql.defaultSizeInBytes")
+    .internal()
+    .doc("The default table size used in query planning. By default, it is set to Long.MaxValue " +
+      "which is larger than `spark.sql.autoBroadcastJoinThreshold` to be more conservative. " +
+      "That is to say by default the optimizer will not choose to broadcast a table unless it " +
+      "knows for sure its size is small enough.")
+    .longConf
+    .createWithDefault(Long.MaxValue)
+
+  val SHUFFLE_PARTITIONS = SQLConfigBuilder("spark.sql.shuffle.partitions")
+    .doc("The default number of partitions to use when shuffling data for joins or aggregations.")
+    .intConf
+    .createWithDefault(200)
+
+  val SHUFFLE_TARGET_POSTSHUFFLE_INPUT_SIZE =
+    SQLConfigBuilder("spark.sql.adaptive.shuffle.targetPostShuffleInputSize")
+      .doc("The target post-shuffle input size in bytes of a task.")
+      .bytesConf(ByteUnit.BYTE)
+      .createWithDefault(64 * 1024 * 1024)
+
+  val ADAPTIVE_EXECUTION_ENABLED = SQLConfigBuilder("spark.sql.adaptive.enabled")
+    .doc("When true, enable adaptive query execution.")
+    .booleanConf
+    .createWithDefault(false)
+
+  val SHUFFLE_MIN_NUM_POSTSHUFFLE_PARTITIONS =
+    SQLConfigBuilder("spark.sql.adaptive.minNumPostShufflePartitions")
+      .internal()
+      .doc("The advisory minimal number of post-shuffle partitions provided to " +
+        "ExchangeCoordinator. This setting is used in our test to make sure we " +
+        "have enough parallelism to expose issues that will not be exposed with a " +
+        "single partition. When the value is a non-positive value, this setting will " +
+        "not be provided to ExchangeCoordinator.")
+      .intConf
+      .createWithDefault(-1)
+
+  val SUBEXPRESSION_ELIMINATION_ENABLED =
+    SQLConfigBuilder("spark.sql.subexpressionElimination.enabled")
+      .internal()
+      .doc("When true, common subexpressions will be eliminated.")
+      .booleanConf
+      .createWithDefault(true)
+
+  val CASE_SENSITIVE = SQLConfigBuilder("spark.sql.caseSensitive")
+    .internal()
+    .doc("Whether the query analyzer should be case sensitive or not. " +
+      "Default to case insensitive. It is highly discouraged to turn on case sensitive mode.")
+    .booleanConf
+    .createWithDefault(false)
+
+  val PARQUET_SCHEMA_MERGING_ENABLED = SQLConfigBuilder("spark.sql.parquet.mergeSchema")
+    .doc("When true, the Parquet data source merges schemas collected from all data files, " +
+         "otherwise the schema is picked from the summary file or a random data file " +
+         "if no summary file is available.")
+    .booleanConf
+    .createWithDefault(false)
+
+  val PARQUET_SCHEMA_RESPECT_SUMMARIES = SQLConfigBuilder("spark.sql.parquet.respectSummaryFiles")
+    .doc("When true, we make assumption that all part-files of Parquet are consistent with " +
+         "summary files and we will ignore them when merging schema. Otherwise, if this is " +
+         "false, which is the default, we will merge all part-files. This should be considered " +
+         "as expert-only option, and shouldn't be enabled before knowing what it means exactly.")
+    .booleanConf
+    .createWithDefault(false)
+
+  val PARQUET_BINARY_AS_STRING = SQLConfigBuilder("spark.sql.parquet.binaryAsString")
+    .doc("Some other Parquet-producing systems, in particular Impala and older versions of " +
+      "Spark SQL, do not differentiate between binary data and strings when writing out the " +
+      "Parquet schema. This flag tells Spark SQL to interpret binary data as a string to provide " +
+      "compatibility with these systems.")
+    .booleanConf
+    .createWithDefault(false)
+
+  val PARQUET_INT96_AS_TIMESTAMP = SQLConfigBuilder("spark.sql.parquet.int96AsTimestamp")
+    .doc("Some Parquet-producing systems, in particular Impala, store Timestamp into INT96. " +
+      "Spark would also store Timestamp as INT96 because we need to avoid precision lost of the " +
+      "nanoseconds field. This flag tells Spark SQL to interpret INT96 data as a timestamp to " +
+      "provide compatibility with these systems.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val PARQUET_CACHE_METADATA = SQLConfigBuilder("spark.sql.parquet.cacheMetadata")
+    .doc("Turns on caching of Parquet schema metadata. Can speed up querying of static data.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val PARQUET_COMPRESSION = SQLConfigBuilder("spark.sql.parquet.compression.codec")
+    .doc("Sets the compression codec use when writing Parquet files. Acceptable values include: " +
+      "uncompressed, snappy, gzip, lzo.")
+    .stringConf
+    .transform(_.toLowerCase())
+    .checkValues(Set("uncompressed", "snappy", "gzip", "lzo"))
+    .createWithDefault("snappy")
+
+  val PARQUET_FILTER_PUSHDOWN_ENABLED = SQLConfigBuilder("spark.sql.parquet.filterPushdown")
+    .doc("Enables Parquet filter push-down optimization when set to true.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val PARQUET_WRITE_LEGACY_FORMAT = SQLConfigBuilder("spark.sql.parquet.writeLegacyFormat")
+    .doc("Whether to follow Parquet's format specification when converting Parquet schema to " +
+      "Spark SQL schema and vice versa.")
+    .booleanConf
+    .createWithDefault(false)
+
+  val PARQUET_OUTPUT_COMMITTER_CLASS = SQLConfigBuilder("spark.sql.parquet.output.committer.class")
+    .doc("The output committer class used by Parquet. The specified class needs to be a " +
+      "subclass of org.apache.hadoop.mapreduce.OutputCommitter.  Typically, it's also a subclass " +
+      "of org.apache.parquet.hadoop.ParquetOutputCommitter.")
+    .internal()
+    .stringConf
+    .createWithDefault("org.apache.parquet.hadoop.ParquetOutputCommitter")
+
+  val PARQUET_VECTORIZED_READER_ENABLED =
+    SQLConfigBuilder("spark.sql.parquet.enableVectorizedReader")
+      .doc("Enables vectorized parquet decoding.")
+      .booleanConf
+      .createWithDefault(true)
+
+  val ORC_FILTER_PUSHDOWN_ENABLED = SQLConfigBuilder("spark.sql.orc.filterPushdown")
+    .doc("When true, enable filter pushdown for ORC files.")
+    .booleanConf
+    .createWithDefault(false)
+
+  val HIVE_VERIFY_PARTITION_PATH = SQLConfigBuilder("spark.sql.hive.verifyPartitionPath")
+    .doc("When true, check all the partition paths under the table\'s root directory " +
+         "when reading data stored in HDFS.")
+    .booleanConf
+    .createWithDefault(false)
+
+  val HIVE_METASTORE_PARTITION_PRUNING =
+    SQLConfigBuilder("spark.sql.hive.metastorePartitionPruning")
+      .doc("When true, some predicates will be pushed down into the Hive metastore so that " +
+           "unmatching partitions can be eliminated earlier. This only affects Hive tables " +
+           "not converted to filesource relations (see HiveUtils.CONVERT_METASTORE_PARQUET and " +
+           "HiveUtils.CONVERT_METASTORE_ORC for more information).")
+      .booleanConf
+      .createWithDefault(true)
+
+  val HIVE_MANAGE_FILESOURCE_PARTITIONS =
+    SQLConfigBuilder("spark.sql.hive.manageFilesourcePartitions")
+      .doc("When true, enable metastore partition management for file source tables as well. " +
+           "This includes both datasource and converted Hive tables. When partition managment " +
+           "is enabled, datasource tables store partition in the Hive metastore, and use the " +
+           "metastore to prune partitions during query planning.")
+      .booleanConf
+      .createWithDefault(true)
+
+  val HIVE_FILESOURCE_PARTITION_FILE_CACHE_SIZE =
+    SQLConfigBuilder("spark.sql.hive.filesourcePartitionFileCacheSize")
+      .doc("When nonzero, enable caching of partition file metadata in memory. All tables share " +
+           "a cache that can use up to specified num bytes for file metadata. This conf only " +
+           "has an effect when hive filesource partition management is enabled.")
+      .longConf
+      .createWithDefault(250 * 1024 * 1024)
+
+  object HiveCaseSensitiveInferenceMode extends Enumeration {
+    val INFER_AND_SAVE, INFER_ONLY, NEVER_INFER = Value
+  }
+
+  val HIVE_CASE_SENSITIVE_INFERENCE = SQLConfigBuilder("spark.sql.hive.caseSensitiveInferenceMode")
+    .doc("Sets the action to take when a case-sensitive schema cannot be read from a Hive " +
+      "table's properties. Although Spark SQL itself is not case-sensitive, Hive compatible file " +
+      "formats such as Parquet are. Spark SQL must use a case-preserving schema when querying " +
+      "any table backed by files containing case-sensitive field names or queries may not return " +
+      "accurate results. Valid options include INFER_AND_SAVE (the default mode-- infer the " +
+      "case-sensitive schema from the underlying data files and write it back to the table " +
+      "properties), INFER_ONLY (infer the schema but don't attempt to write it to the table " +
+      "properties) and NEVER_INFER (fallback to using the case-insensitive metastore schema " +
+      "instead of inferring).")
+    .stringConf
+    .transform(_.toUpperCase())
+    .checkValues(HiveCaseSensitiveInferenceMode.values.map(_.toString))
+    .createWithDefault(HiveCaseSensitiveInferenceMode.INFER_AND_SAVE.toString)
+
+  val OPTIMIZER_METADATA_ONLY = SQLConfigBuilder("spark.sql.optimizer.metadataOnly")
+    .doc("When true, enable the metadata-only query optimization that use the table's metadata " +
+      "to produce the partition columns instead of table scans. It applies when all the columns " +
+      "scanned are partition columns and the query has an aggregate operator that satisfies " +
+      "distinct semantics.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val COLUMN_NAME_OF_CORRUPT_RECORD = SQLConfigBuilder("spark.sql.columnNameOfCorruptRecord")
+    .doc("The name of internal column for storing raw/un-parsed JSON records that fail to parse.")
+    .stringConf
+    .createWithDefault("_corrupt_record")
+
+  val BROADCAST_TIMEOUT = SQLConfigBuilder("spark.sql.broadcastTimeout")
+    .doc("Timeout in seconds for the broadcast wait time in broadcast joins.")
+    .intConf
+    .createWithDefault(5 * 60)
+
+  // This is only used for the thriftserver
+  val THRIFTSERVER_POOL = SQLConfigBuilder("spark.sql.thriftserver.scheduler.pool")
+    .doc("Set a Fair Scheduler pool for a JDBC client session.")
+    .stringConf
+    .createOptional
+
+  val THRIFTSERVER_INCREMENTAL_COLLECT =
+    SQLConfigBuilder("spark.sql.thriftServer.incrementalCollect")
+      .internal()
+      .doc("When true, enable incremental collection for execution in Thrift Server.")
+      .booleanConf
+      .createWithDefault(false)
+
+  val THRIFTSERVER_UI_STATEMENT_LIMIT =
+    SQLConfigBuilder("spark.sql.thriftserver.ui.retainedStatements")
+      .doc("The number of SQL statements kept in the JDBC/ODBC web UI history.")
+      .intConf
+      .createWithDefault(200)
+
+  val THRIFTSERVER_UI_SESSION_LIMIT = SQLConfigBuilder("spark.sql.thriftserver.ui.retainedSessions")
+    .doc("The number of SQL client sessions kept in the JDBC/ODBC web UI history.")
+    .intConf
+    .createWithDefault(200)
+
+  // This is used to set the default data source
+  val DEFAULT_DATA_SOURCE_NAME = SQLConfigBuilder("spark.sql.sources.default")
+    .doc("The default data source to use in input/output.")
+    .stringConf
+    .createWithDefault("parquet")
+
+  val CONVERT_CTAS = SQLConfigBuilder("spark.sql.hive.convertCTAS")
+    .internal()
+    .doc("When true, a table created by a Hive CTAS statement (no USING clause) " +
+      "without specifying any storage property will be converted to a data source table, " +
+      "using the data source set by spark.sql.sources.default.")
+    .booleanConf
+    .createWithDefault(false)
+
+  val GATHER_FASTSTAT = SQLConfigBuilder("spark.sql.hive.gatherFastStats")
+      .internal()
+      .doc("When true, fast stats (number of files and total size of all files) will be gathered" +
+        " in parallel while repairing table partitions to avoid the sequential listing in Hive" +
+        " metastore.")
+      .booleanConf
+      .createWithDefault(true)
+
+  val PARTITION_COLUMN_TYPE_INFERENCE =
+    SQLConfigBuilder("spark.sql.sources.partitionColumnTypeInference.enabled")
+      .doc("When true, automatically infer the data types for partitioned columns.")
+      .booleanConf
+      .createWithDefault(true)
+
+  val BUCKETING_ENABLED = SQLConfigBuilder("spark.sql.sources.bucketing.enabled")
+    .doc("When false, we will treat bucketed table as normal table")
+    .booleanConf
+    .createWithDefault(true)
+
+  val CROSS_JOINS_ENABLED = SQLConfigBuilder("spark.sql.crossJoin.enabled")
+    .doc("When false, we will throw an error if a query contains a cartesian product without " +
+        "explicit CROSS JOIN syntax.")
+    .booleanConf
+    .createWithDefault(false)
+
+  val ORDER_BY_ORDINAL = SQLConfigBuilder("spark.sql.orderByOrdinal")
+    .doc("When true, the ordinal numbers are treated as the position in the select list. " +
+         "When false, the ordinal numbers in order/sort by clause are ignored.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val GROUP_BY_ORDINAL = SQLConfigBuilder("spark.sql.groupByOrdinal")
+    .doc("When true, the ordinal numbers in group by clauses are treated as the position " +
+      "in the select list. When false, the ordinal numbers are ignored.")
+    .booleanConf
+    .createWithDefault(true)
+
+  // The output committer class used by data sources. The specified class needs to be a
+  // subclass of org.apache.hadoop.mapreduce.OutputCommitter.
+  val OUTPUT_COMMITTER_CLASS =
+    SQLConfigBuilder("spark.sql.sources.outputCommitterClass").internal().stringConf.createOptional
+
+  val FILE_COMMIT_PROTOCOL_CLASS =
+    SQLConfigBuilder("spark.sql.sources.commitProtocolClass")
+      .internal()
+      .stringConf
+      .createWithDefault(
+        "org.apache.spark.sql.execution.datasources.SQLHadoopMapReduceCommitProtocol")
+
+  val PARALLEL_PARTITION_DISCOVERY_THRESHOLD =
+    SQLConfigBuilder("spark.sql.sources.parallelPartitionDiscovery.threshold")
+      .doc("The maximum number of files allowed for listing files at driver side. If the number " +
+        "of detected files exceeds this value during partition discovery, it tries to list the " +
+        "files with another Spark distributed job. This applies to Parquet, ORC, CSV, JSON and " +
+        "LibSVM data sources.")
+      .intConf
+      .createWithDefault(32)
+
+  val PARALLEL_PARTITION_DISCOVERY_PARALLELISM =
+    SQLConfigBuilder("spark.sql.sources.parallelPartitionDiscovery.parallelism")
+      .doc("The number of parallelism to list a collection of path recursively, Set the " +
+        "number to prevent file listing from generating too many tasks.")
+      .internal()
+      .intConf
+      .createWithDefault(10000)
+
+  // Whether to automatically resolve ambiguity in join conditions for self-joins.
+  // See SPARK-6231.
+  val DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY =
+    SQLConfigBuilder("spark.sql.selfJoinAutoResolveAmbiguity")
+      .internal()
+      .booleanConf
+      .createWithDefault(true)
+
+  // Whether to retain group by columns or not in GroupedData.agg.
+  val DATAFRAME_RETAIN_GROUP_COLUMNS = SQLConfigBuilder("spark.sql.retainGroupColumns")
+    .internal()
+    .booleanConf
+    .createWithDefault(true)
+
+  val DATAFRAME_PIVOT_MAX_VALUES = SQLConfigBuilder("spark.sql.pivotMaxValues")
+    .doc("When doing a pivot without specifying values for the pivot column this is the maximum " +
+      "number of (distinct) values that will be collected without error.")
+    .intConf
+    .createWithDefault(10000)
+
+  val RUN_SQL_ON_FILES = SQLConfigBuilder("spark.sql.runSQLOnFiles")
+    .internal()
+    .doc("When true, we could use `datasource`.`path` as table in SQL query.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val WHOLESTAGE_CODEGEN_ENABLED = SQLConfigBuilder("spark.sql.codegen.wholeStage")
+    .internal()
+    .doc("When true, the whole stage (of multiple operators) will be compiled into single java" +
+      " method.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val WHOLESTAGE_MAX_NUM_FIELDS = SQLConfigBuilder("spark.sql.codegen.maxFields")
+    .internal()
+    .doc("The maximum number of fields (including nested fields) that will be supported before" +
+      " deactivating whole-stage codegen.")
+    .intConf
+    .createWithDefault(100)
+
+  val WHOLESTAGE_FALLBACK = SQLConfigBuilder("spark.sql.codegen.fallback")
+    .internal()
+    .doc("When true, whole stage codegen could be temporary disabled for the part of query that" +
+      " fail to compile generated code")
+    .booleanConf
+    .createWithDefault(true)
+
+  val MAX_CASES_BRANCHES = SQLConfigBuilder("spark.sql.codegen.maxCaseBranches")
+    .internal()
+    .doc("The maximum number of switches supported with codegen.")
+    .intConf
+    .createWithDefault(20)
+
+  val FILES_MAX_PARTITION_BYTES = SQLConfigBuilder("spark.sql.files.maxPartitionBytes")
+    .doc("The maximum number of bytes to pack into a single partition when reading files.")
+    .longConf
+    .createWithDefault(128 * 1024 * 1024) // parquet.block.size
+
+  val FILES_OPEN_COST_IN_BYTES = SQLConfigBuilder("spark.sql.files.openCostInBytes")
+    .internal()
+    .doc("The estimated cost to open a file, measured by the number of bytes could be scanned in" +
+      " the same time. This is used when putting multiple files into a partition. It's better to" +
+      " over estimated, then the partitions with small files will be faster than partitions with" +
+      " bigger files (which is scheduled first).")
+    .longConf
+    .createWithDefault(4 * 1024 * 1024)
+
+  val EXCHANGE_REUSE_ENABLED = SQLConfigBuilder("spark.sql.exchange.reuse")
+    .internal()
+    .doc("When true, the planner will try to find out duplicated exchanges and re-use them.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val STATE_STORE_MIN_DELTAS_FOR_SNAPSHOT =
+    SQLConfigBuilder("spark.sql.streaming.stateStore.minDeltasForSnapshot")
+      .internal()
+      .doc("Minimum number of state store delta files that needs to be generated before they " +
+        "consolidated into snapshots.")
+      .intConf
+      .createWithDefault(10)
+
+  val CHECKPOINT_LOCATION = SQLConfigBuilder("spark.sql.streaming.checkpointLocation")
+    .doc("The default location for storing checkpoint data for streaming queries.")
+    .stringConf
+    .createOptional
+
+  val MIN_BATCHES_TO_RETAIN = SQLConfigBuilder("spark.sql.streaming.minBatchesToRetain")
+    .internal()
+    .doc("The minimum number of batches that must be retained and made recoverable.")
+    .intConf
+    .createWithDefault(100)
+
+  val UNSUPPORTED_OPERATION_CHECK_ENABLED =
+    SQLConfigBuilder("spark.sql.streaming.unsupportedOperationCheck")
+      .internal()
+      .doc("When true, the logical plan for streaming query will be checked for unsupported" +
+        " operations.")
+      .booleanConf
+      .createWithDefault(true)
+
+  val VARIABLE_SUBSTITUTE_ENABLED =
+    SQLConfigBuilder("spark.sql.variable.substitute")
+      .doc("This enables substitution using syntax like ${var} ${system:var} and ${env:var}.")
+      .booleanConf
+      .createWithDefault(true)
+
+  val VARIABLE_SUBSTITUTE_DEPTH =
+    SQLConfigBuilder("spark.sql.variable.substitute.depth")
+      .internal()
+      .doc("Deprecated: The maximum replacements the substitution engine will do.")
+      .intConf
+      .createWithDefault(40)
+
+  val ENABLE_TWOLEVEL_AGG_MAP =
+    SQLConfigBuilder("spark.sql.codegen.aggregate.map.twolevel.enable")
+      .internal()
+      .doc("Enable two-level aggregate hash map. When enabled, records will first be " +
+        "inserted/looked-up at a 1st-level, small, fast map, and then fallback to a " +
+        "2nd-level, larger, slower map when 1st level is full or keys cannot be found. " +
+        "When disabled, records go directly to the 2nd level. Defaults to true.")
+      .booleanConf
+      .createWithDefault(true)
+
+  val STREAMING_FILE_COMMIT_PROTOCOL_CLASS =
+    SQLConfigBuilder("spark.sql.streaming.commitProtocolClass")
+      .internal()
+      .stringConf
+      .createWithDefault("org.apache.spark.sql.execution.streaming.ManifestFileCommitProtocol")
+
+  val FILE_SINK_LOG_DELETION = SQLConfigBuilder("spark.sql.streaming.fileSink.log.deletion")
+    .internal()
+    .doc("Whether to delete the expired log files in file stream sink.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val FILE_SINK_LOG_COMPACT_INTERVAL =
+    SQLConfigBuilder("spark.sql.streaming.fileSink.log.compactInterval")
+      .internal()
+      .doc("Number of log files after which all the previous files " +
+        "are compacted into the next log file.")
+      .intConf
+      .createWithDefault(10)
+
+  val FILE_SINK_LOG_CLEANUP_DELAY =
+    SQLConfigBuilder("spark.sql.streaming.fileSink.log.cleanupDelay")
+      .internal()
+      .doc("How long that a file is guaranteed to be visible for all readers.")
+      .timeConf(TimeUnit.MILLISECONDS)
+      .createWithDefault(TimeUnit.MINUTES.toMillis(10)) // 10 minutes
+
+  val FILE_SOURCE_LOG_DELETION = SQLConfigBuilder("spark.sql.streaming.fileSource.log.deletion")
+    .internal()
+    .doc("Whether to delete the expired log files in file stream source.")
+    .booleanConf
+    .createWithDefault(true)
+
+  val FILE_SOURCE_LOG_COMPACT_INTERVAL =
+    SQLConfigBuilder("spark.sql.streaming.fileSource.log.compactInterval")
+      .internal()
+      .doc("Number of log files after which all the previous files " +
+        "are compacted into the next log file.")
+      .intConf
+      .createWithDefault(10)
+
+  val FILE_SOURCE_LOG_CLEANUP_DELAY =
+    SQLConfigBuilder("spark.sql.streaming.fileSource.log.cleanupDelay")
+      .internal()
+      .doc("How long in milliseconds a file is guaranteed to be visible for all readers.")
+      .timeConf(TimeUnit.MILLISECONDS)
+      .createWithDefault(TimeUnit.MINUTES.toMillis(10)) // 10 minutes
+
+  val STREAMING_SCHEMA_INFERENCE =
+    SQLConfigBuilder("spark.sql.streaming.schemaInference")
+      .internal()
+      .doc("Whether file-based streaming sources will infer its own schema")
+      .booleanConf
+      .createWithDefault(false)
+
+  val STREAMING_POLLING_DELAY =
+    SQLConfigBuilder("spark.sql.streaming.pollingDelay")
+      .internal()
+      .doc("How long to delay polling new data when no data is available")
+      .timeConf(TimeUnit.MILLISECONDS)
+      .createWithDefault(10L)
+
+  val STREAMING_NO_DATA_PROGRESS_EVENT_INTERVAL =
+    SQLConfigBuilder("spark.sql.streaming.noDataProgressEventInterval")
+      .internal()
+      .doc("How long to wait between two progress events when there is no data")
+      .timeConf(TimeUnit.MILLISECONDS)
+      .createWithDefault(10000L)
+
+  val STREAMING_METRICS_ENABLED =
+    SQLConfigBuilder("spark.sql.streaming.metricsEnabled")
+      .doc("Whether Dropwizard/Codahale metrics will be reported for active streaming queries.")
+      .booleanConf
+      .createWithDefault(false)
+
+  val STREAMING_PROGRESS_RETENTION =
+    SQLConfigBuilder("spark.sql.streaming.numRecentProgressUpdates")
+      .doc("The number of progress updates to retain for a streaming query")
+      .intConf
+      .createWithDefault(100)
+
+  val NDV_MAX_ERROR =
+    SQLConfigBuilder("spark.sql.statistics.ndv.maxError")
+      .internal()
+      .doc("The maximum estimation error allowed in HyperLogLog++ algorithm when generating " +
+        "column level statistics.")
+      .doubleConf
+      .createWithDefault(0.05)
+
+  val IGNORE_CORRUPT_FILES = SQLConfigBuilder("spark.sql.files.ignoreCorruptFiles")
+    .doc("Whether to ignore corrupt files. If true, the Spark jobs will continue to run when " +
+      "encountering corrupted or non-existing and contents that have been read will still be " +
+      "returned.")
+    .booleanConf
+    .createWithDefault(false)
+
+  object Deprecated {
+    val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
+  }
+}
+
+/**
+ * A class that enables the setting and getting of mutable config parameters/hints.
+ *
+ * In the presence of a SQLContext, these can be set and queried by passing SET commands
+ * into Spark SQL's query functions (i.e. sql()). Otherwise, users of this class can
+ * modify the hints by programmatically calling the setters and getters of this class.
+ *
+ * SQLConf is thread-safe (internally synchronized, so safe to be used in multiple threads).
+ */
+class SQLConf extends Serializable with Logging {
+  import SQLConf._
+
+  /** Only low degree of contention is expected for conf, thus NOT using ConcurrentHashMap. */
+  @transient protected[spark] val settings = java.util.Collections.synchronizedMap(
+    new java.util.HashMap[String, String]())
+
+  @transient private val reader = new ConfigReader(settings)
+
+  /** ************************ Spark SQL Params/Hints ******************* */
+
+  def optimizerMaxIterations: Int = getConf(OPTIMIZER_MAX_ITERATIONS)
+
+  def optimizerInSetConversionThreshold: Int = getConf(OPTIMIZER_INSET_CONVERSION_THRESHOLD)
+
+  def stateStoreMinDeltasForSnapshot: Int = getConf(STATE_STORE_MIN_DELTAS_FOR_SNAPSHOT)
+
+  def checkpointLocation: Option[String] = getConf(CHECKPOINT_LOCATION)
+
+  def isUnsupportedOperationCheckEnabled: Boolean = getConf(UNSUPPORTED_OPERATION_CHECK_ENABLED)
+
+  def streamingFileCommitProtocolClass: String = getConf(STREAMING_FILE_COMMIT_PROTOCOL_CLASS)
+
+  def fileSinkLogDeletion: Boolean = getConf(FILE_SINK_LOG_DELETION)
+
+  def fileSinkLogCompactInterval: Int = getConf(FILE_SINK_LOG_COMPACT_INTERVAL)
+
+  def fileSinkLogCleanupDelay: Long = getConf(FILE_SINK_LOG_CLEANUP_DELAY)
+
+  def fileSourceLogDeletion: Boolean = getConf(FILE_SOURCE_LOG_DELETION)
+
+  def fileSourceLogCompactInterval: Int = getConf(FILE_SOURCE_LOG_COMPACT_INTERVAL)
+
+  def fileSourceLogCleanupDelay: Long = getConf(FILE_SOURCE_LOG_CLEANUP_DELAY)
+
+  def streamingSchemaInference: Boolean = getConf(STREAMING_SCHEMA_INFERENCE)
+
+  def streamingPollingDelay: Long = getConf(STREAMING_POLLING_DELAY)
+
+  def streamingNoDataProgressEventInterval: Long =
+    getConf(STREAMING_NO_DATA_PROGRESS_EVENT_INTERVAL)
+
+  def streamingMetricsEnabled: Boolean = getConf(STREAMING_METRICS_ENABLED)
+
+  def streamingProgressRetention: Int = getConf(STREAMING_PROGRESS_RETENTION)
+
+  def filesMaxPartitionBytes: Long = getConf(FILES_MAX_PARTITION_BYTES)
+
+  def filesOpenCostInBytes: Long = getConf(FILES_OPEN_COST_IN_BYTES)
+
+  def useCompression: Boolean = getConf(COMPRESS_CACHED)
+
+  def parquetCompressionCodec: String = getConf(PARQUET_COMPRESSION)
+
+  def parquetCacheMetadata: Boolean = getConf(PARQUET_CACHE_METADATA)
+
+  def parquetVectorizedReaderEnabled: Boolean = getConf(PARQUET_VECTORIZED_READER_ENABLED)
+
+  def columnBatchSize: Int = getConf(COLUMN_BATCH_SIZE)
+
+  def numShufflePartitions: Int = getConf(SHUFFLE_PARTITIONS)
+
+  def targetPostShuffleInputSize: Long =
+    getConf(SHUFFLE_TARGET_POSTSHUFFLE_INPUT_SIZE)
+
+  def adaptiveExecutionEnabled: Boolean = getConf(ADAPTIVE_EXECUTION_ENABLED)
+
+  def minNumPostShufflePartitions: Int =
+    getConf(SHUFFLE_MIN_NUM_POSTSHUFFLE_PARTITIONS)
+
+  def minBatchesToRetain: Int = getConf(MIN_BATCHES_TO_RETAIN)
+
+  def parquetFilterPushDown: Boolean = getConf(PARQUET_FILTER_PUSHDOWN_ENABLED)
+
+  def orcFilterPushDown: Boolean = getConf(ORC_FILTER_PUSHDOWN_ENABLED)
+
+  def verifyPartitionPath: Boolean = getConf(HIVE_VERIFY_PARTITION_PATH)
+
+  def metastorePartitionPruning: Boolean = getConf(HIVE_METASTORE_PARTITION_PRUNING)
+
+  def manageFilesourcePartitions: Boolean = getConf(HIVE_MANAGE_FILESOURCE_PARTITIONS)
+
+  def filesourcePartitionFileCacheSize: Long = getConf(HIVE_FILESOURCE_PARTITION_FILE_CACHE_SIZE)
+
+  def caseSensitiveInferenceMode: HiveCaseSensitiveInferenceMode.Value =
+    HiveCaseSensitiveInferenceMode.withName(getConf(HIVE_CASE_SENSITIVE_INFERENCE))
+
+  def gatherFastStats: Boolean = getConf(GATHER_FASTSTAT)
+
+  def optimizerMetadataOnly: Boolean = getConf(OPTIMIZER_METADATA_ONLY)
+
+  def wholeStageEnabled: Boolean = getConf(WHOLESTAGE_CODEGEN_ENABLED)
+
+  def wholeStageMaxNumFields: Int = getConf(WHOLESTAGE_MAX_NUM_FIELDS)
+
+  def wholeStageFallback: Boolean = getConf(WHOLESTAGE_FALLBACK)
+
+  def maxCaseBranchesForCodegen: Int = getConf(MAX_CASES_BRANCHES)
+
+  def exchangeReuseEnabled: Boolean = getConf(EXCHANGE_REUSE_ENABLED)
+
+  def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE)
+
+  /**
+   * Returns the [[Resolver]] for the current configuration, which can be used to determine if two
+   * identifiers are equal.
+   */
+  def resolver: Resolver = {
+    if (caseSensitiveAnalysis) {
+      org.apache.spark.sql.catalyst.analysis.caseSensitiveResolution
+    } else {
+      org.apache.spark.sql.catalyst.analysis.caseInsensitiveResolution
+    }
+  }
+
+  def subexpressionEliminationEnabled: Boolean =
+    getConf(SUBEXPRESSION_ELIMINATION_ENABLED)
+
+  def autoBroadcastJoinThreshold: Long = getConf(AUTO_BROADCASTJOIN_THRESHOLD)
+
+  def limitScaleUpFactor: Int = getConf(LIMIT_SCALE_UP_FACTOR)
+
+  def fallBackToHdfsForStatsEnabled: Boolean = getConf(ENABLE_FALL_BACK_TO_HDFS_FOR_STATS)
+
+  def preferSortMergeJoin: Boolean = getConf(PREFER_SORTMERGEJOIN)
+
+  def enableRadixSort: Boolean = getConf(RADIX_SORT_ENABLED)
+
+  def defaultSizeInBytes: Long = getConf(DEFAULT_SIZE_IN_BYTES)
+
+  def isParquetSchemaMergingEnabled: Boolean = getConf(PARQUET_SCHEMA_MERGING_ENABLED)
+
+  def isParquetSchemaRespectSummaries: Boolean = getConf(PARQUET_SCHEMA_RESPECT_SUMMARIES)
+
+  def parquetOutputCommitterClass: String = getConf(PARQUET_OUTPUT_COMMITTER_CLASS)
+
+  def isParquetBinaryAsString: Boolean = getConf(PARQUET_BINARY_AS_STRING)
+
+  def isParquetINT96AsTimestamp: Boolean = getConf(PARQUET_INT96_AS_TIMESTAMP)
+
+  def writeLegacyParquetFormat: Boolean = getConf(PARQUET_WRITE_LEGACY_FORMAT)
+
+  def inMemoryPartitionPruning: Boolean = getConf(IN_MEMORY_PARTITION_PRUNING)
+
+  def columnNameOfCorruptRecord: String = getConf(COLUMN_NAME_OF_CORRUPT_RECORD)
+
+  def broadcastTimeout: Int = getConf(BROADCAST_TIMEOUT)
+
+  def defaultDataSourceName: String = getConf(DEFAULT_DATA_SOURCE_NAME)
+
+  def convertCTAS: Boolean = getConf(CONVERT_CTAS)
+
+  def partitionColumnTypeInferenceEnabled: Boolean =
+    getConf(SQLConf.PARTITION_COLUMN_TYPE_INFERENCE)
+
+  def fileCommitProtocolClass: String = getConf(SQLConf.FILE_COMMIT_PROTOCOL_CLASS)
+
+  def parallelPartitionDiscoveryThreshold: Int =
+    getConf(SQLConf.PARALLEL_PARTITION_DISCOVERY_THRESHOLD)
+
+  def parallelPartitionDiscoveryParallelism: Int =
+    getConf(SQLConf.PARALLEL_PARTITION_DISCOVERY_PARALLELISM)
+
+  def bucketingEnabled: Boolean = getConf(SQLConf.BUCKETING_ENABLED)
+
+  def dataFrameSelfJoinAutoResolveAmbiguity: Boolean =
+    getConf(DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY)
+
+  def dataFrameRetainGroupColumns: Boolean = getConf(DATAFRAME_RETAIN_GROUP_COLUMNS)
+
+  def dataFramePivotMaxValues: Int = getConf(DATAFRAME_PIVOT_MAX_VALUES)
+
+  def runSQLonFile: Boolean = getConf(RUN_SQL_ON_FILES)
+
+  def enableTwoLevelAggMap: Boolean = getConf(ENABLE_TWOLEVEL_AGG_MAP)
+
+  def variableSubstituteEnabled: Boolean = getConf(VARIABLE_SUBSTITUTE_ENABLED)
+
+  def variableSubstituteDepth: Int = getConf(VARIABLE_SUBSTITUTE_DEPTH)
+
+  def warehousePath: String = new Path(getConf(StaticSQLConf.WAREHOUSE_PATH)).toString
+
+  def ignoreCorruptFiles: Boolean = getConf(IGNORE_CORRUPT_FILES)
+
+  def orderByOrdinal: Boolean = getConf(ORDER_BY_ORDINAL)
+
+  def groupByOrdinal: Boolean = getConf(GROUP_BY_ORDINAL)
+
+  def crossJoinEnabled: Boolean = getConf(SQLConf.CROSS_JOINS_ENABLED)
+
+  def ndvMaxError: Double = getConf(NDV_MAX_ERROR)
+  /** ********************** SQLConf functionality methods ************ */
+
+  /** Set Spark SQL configuration properties. */
+  def setConf(props: Properties): Unit = settings.synchronized {
+    props.asScala.foreach { case (k, v) => setConfString(k, v) }
+  }
+
+  /** Set the given Spark SQL configuration property using a `string` value. */
+  def setConfString(key: String, value: String): Unit = {
+    require(key != null, "key cannot be null")
+    require(value != null, s"value cannot be null for key: $key")
+    val entry = sqlConfEntries.get(key)
+    if (entry != null) {
+      // Only verify configs in the SQLConf object
+      entry.valueConverter(value)
+    }
+    setConfWithCheck(key, value)
+  }
+
+  /** Set the given Spark SQL configuration property. */
+  def setConf[T](entry: ConfigEntry[T], value: T): Unit = {
+    require(entry != null, "entry cannot be null")
+    require(value != null, s"value cannot be null for key: ${entry.key}")
+    require(sqlConfEntries.get(entry.key) == entry, s"$entry is not registered")
+    setConfWithCheck(entry.key, entry.stringConverter(value))
+  }
+
+  /** Return the value of Spark SQL configuration property for the given key. */
+  @throws[NoSuchElementException]("if key is not set")
+  def getConfString(key: String): String = {
+    Option(settings.get(key)).
+      orElse {
+        // Try to use the default value
+        Option(sqlConfEntries.get(key)).map(_.defaultValueString)
+      }.
+      getOrElse(throw new NoSuchElementException(key))
+  }
+
+  /**
+   * Return the value of Spark SQL configuration property for the given key. If the key is not set
+   * yet, return `defaultValue`. This is useful when `defaultValue` in ConfigEntry is not the
+   * desired one.
+   */
+  def getConf[T](entry: ConfigEntry[T], defaultValue: T): T = {
+    require(sqlConfEntries.get(entry.key) == entry, s"$entry is not registered")
+    Option(settings.get(entry.key)).map(entry.valueConverter).getOrElse(defaultValue)
+  }
+
+  /**
+   * Return the value of Spark SQL configuration property for the given key. If the key is not set
+   * yet, return `defaultValue` in [[ConfigEntry]].
+   */
+  def getConf[T](entry: ConfigEntry[T]): T = {
+    require(sqlConfEntries.get(entry.key) == entry, s"$entry is not registered")
+    entry.readFrom(reader)
+  }
+
+  /**
+   * Return the value of an optional Spark SQL configuration property for the given key. If the key
+   * is not set yet, returns None.
+   */
+  def getConf[T](entry: OptionalConfigEntry[T]): Option[T] = {
+    require(sqlConfEntries.get(entry.key) == entry, s"$entry is not registered")
+    entry.readFrom(reader)
+  }
+
+  /**
+   * Return the `string` value of Spark SQL configuration property for the given key. If the key is
+   * not set yet, return `defaultValue`.
+   */
+  def getConfString(key: String, defaultValue: String): String = {
+    val entry = sqlConfEntries.get(key)
+    if (entry != null && defaultValue != "<undefined>") {
+      // Only verify configs in the SQLConf object
+      entry.valueConverter(defaultValue)
+    }
+    Option(settings.get(key)).getOrElse(defaultValue)
+  }
+
+  /**
+   * Return all the configuration properties that have been set (i.e. not the default).
+   * This creates a new copy of the config properties in the form of a Map.
+   */
+  def getAllConfs: immutable.Map[String, String] =
+    settings.synchronized { settings.asScala.toMap }
+
+  /**
+   * Return all the configuration definitions that have been defined in [[SQLConf]]. Each
+   * definition contains key, defaultValue and doc.
+   */
+  def getAllDefinedConfs: Seq[(String, String, String)] = sqlConfEntries.synchronized {
+    sqlConfEntries.values.asScala.filter(_.isPublic).map { entry =>
+      (entry.key, getConfString(entry.key, entry.defaultValueString), entry.doc)
+    }.toSeq
+  }
+
+  /**
+   * Return whether a given key is set in this [[SQLConf]].
+   */
+  def contains(key: String): Boolean = {
+    settings.containsKey(key)
+  }
+
+  private def setConfWithCheck(key: String, value: String): Unit = {
+    settings.put(key, value)
+  }
+
+  def unsetConf(key: String): Unit = {
+    settings.remove(key)
+  }
+
+  def unsetConf(entry: ConfigEntry[_]): Unit = {
+    settings.remove(entry.key)
+  }
+
+  def clear(): Unit = {
+    settings.clear()
+  }
+}

http://git-wip-us.apache.org/repos/asf/spark/blob/80ebca62/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/StaticSQLConf.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/StaticSQLConf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/StaticSQLConf.scala
new file mode 100644
index 0000000..52ca174
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/StaticSQLConf.scala
@@ -0,0 +1,77 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.sql.internal
+
+import org.apache.spark.internal.config.ConfigBuilder
+import org.apache.spark.util.Utils
+
+
+/**
+ * Static SQL configuration is a cross-session, immutable Spark configuration. External users can
+ * see the static sql configs via `SparkSession.conf`, but can NOT set/unset them.
+ *//**
+ * Static SQL configuration is a cross-session, immutable Spark configuration. External users can
+ * see the static sql configs via `SparkSession.conf`, but can NOT set/unset them.
+ */
+object StaticSQLConf {
+  val globalConfKeys = java.util.Collections.synchronizedSet(new java.util.HashSet[String]())
+
+  private def buildConf(key: String): ConfigBuilder = {
+    ConfigBuilder(key).onCreate { entry =>
+      globalConfKeys.add(entry.key)
+      SQLConf.register(entry)
+    }
+  }
+
+  val WAREHOUSE_PATH = buildConf("spark.sql.warehouse.dir")
+    .doc("The default location for managed databases and tables.")
+    .stringConf
+    .createWithDefault(Utils.resolveURI("spark-warehouse").toString)
+
+  val CATALOG_IMPLEMENTATION = buildConf("spark.sql.catalogImplementation")
+    .internal()
+    .stringConf
+    .checkValues(Set("hive", "in-memory"))
+    .createWithDefault("in-memory")
+
+  val GLOBAL_TEMP_DATABASE = buildConf("spark.sql.globalTempDatabase")
+    .internal()
+    .stringConf
+    .createWithDefault("global_temp")
+
+  // This is used to control when we will split a schema's JSON string to multiple pieces
+  // in order to fit the JSON string in metastore's table property (by default, the value has
+  // a length restriction of 4000 characters, so do not use a value larger than 4000 as the default
+  // value of this property). We will split the JSON string of a schema to its length exceeds the
+  // threshold. Note that, this conf is only read in HiveExternalCatalog which is cross-session,
+  // that's why this conf has to be a static SQL conf.
+  val SCHEMA_STRING_LENGTH_THRESHOLD = buildConf("spark.sql.sources.schemaStringLengthThreshold")
+    .doc("The maximum length allowed in a single cell when " +
+      "storing additional schema information in Hive's metastore.")
+    .internal()
+    .intConf
+    .createWithDefault(4000)
+
+  // When enabling the debug, Spark SQL internal table properties are not filtered out; however,
+  // some related DDL commands (e.g., ANALYZE TABLE and CREATE TABLE LIKE) might not work properly.
+  val DEBUG_MODE = buildConf("spark.sql.debug")
+    .internal()
+    .doc("Only used for internal debugging. Not all functions are supported when it is enabled.")
+    .booleanConf
+    .createWithDefault(false)
+}


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