diff options
| -rw-r--r-- | R/pkg/R/serialize.R | 2 | ||||
| -rwxr-xr-x | conf/spark-env.sh.template | 2 | ||||
| -rw-r--r-- | docs/sql-programming-guide.md | 2 | ||||
| -rw-r--r-- | mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala | 2 | ||||
| -rw-r--r-- | sql/README.md | 2 |
5 files changed, 5 insertions, 5 deletions
diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R index 095ddb9aed..70e87a93e6 100644 --- a/R/pkg/R/serialize.R +++ b/R/pkg/R/serialize.R @@ -54,7 +54,7 @@ writeObject <- function(con, object, writeType = TRUE) { # passing in vectors as arrays and instead require arrays to be passed # as lists. type <- class(object)[[1]] # class of POSIXlt is c("POSIXlt", "POSIXt") - # Checking types is needed here, since ‘is.na’ only handles atomic vectors, + # Checking types is needed here, since 'is.na' only handles atomic vectors, # lists and pairlists if (type %in% c("integer", "character", "logical", "double", "numeric")) { if (is.na(object)) { diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template index 771251f90e..a031cd6a72 100755 --- a/conf/spark-env.sh.template +++ b/conf/spark-env.sh.template @@ -41,7 +41,7 @@ # - SPARK_EXECUTOR_MEMORY, Memory per Executor (e.g. 1000M, 2G) (Default: 1G) # - SPARK_DRIVER_MEMORY, Memory for Driver (e.g. 1000M, 2G) (Default: 1G) # - SPARK_YARN_APP_NAME, The name of your application (Default: Spark) -# - SPARK_YARN_QUEUE, The hadoop queue to use for allocation requests (Default: ‘default’) +# - SPARK_YARN_QUEUE, The hadoop queue to use for allocation requests (Default: 'default') # - SPARK_YARN_DIST_FILES, Comma separated list of files to be distributed with the job. # - SPARK_YARN_DIST_ARCHIVES, Comma separated list of archives to be distributed with the job. diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index ce53a39f9f..d246100f3e 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -2389,7 +2389,7 @@ let user control table caching explicitly: CACHE TABLE logs_last_month; UNCACHE TABLE logs_last_month; -**NOTE:** `CACHE TABLE tbl` is now __eager__ by default not __lazy__. Don’t need to trigger cache materialization manually anymore. +**NOTE:** `CACHE TABLE tbl` is now __eager__ by default not __lazy__. Don't need to trigger cache materialization manually anymore. Spark SQL newly introduced a statement to let user control table caching whether or not lazy since Spark 1.2.0: diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala index 81fc6603cc..3ae108d822 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala @@ -956,7 +956,7 @@ class LinearRegressionSuite V1 -3.7271 2.9032 -1.284 0.3279 V2 3.0100 0.6022 4.998 0.0378 * --- - Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 + Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for gaussian family taken to be 17.4376) diff --git a/sql/README.md b/sql/README.md index a13bdab6d4..9ea271d33d 100644 --- a/sql/README.md +++ b/sql/README.md @@ -5,7 +5,7 @@ This module provides support for executing relational queries expressed in eithe Spark SQL is broken up into four subprojects: - Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions. - - Execution (sql/core) - A query planner / execution engine for translating Catalyst’s logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files. + - Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files. - Hive Support (sql/hive) - Includes an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allows users to run queries that include Hive UDFs, UDAFs, and UDTFs. - HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server. |