diff --git a/datafusion/common/src/scalar/mod.rs b/datafusion/common/src/scalar/mod.rs index bba994dd11b5..8182e4fd47d4 100644 --- a/datafusion/common/src/scalar/mod.rs +++ b/datafusion/common/src/scalar/mod.rs @@ -2551,7 +2551,7 @@ impl ScalarValue { Arc::new(array) } // explicitly enumerate unsupported types so newly added - // types must be aknowledged, Time32 and Time64 types are + // types must be acknowledged, Time32 and Time64 types are // not supported if the TimeUnit is not valid (Time32 can // only be used with Second and Millisecond, Time64 only // with Microsecond and Nanosecond) diff --git a/datafusion/core/src/datasource/listing/table.rs b/datafusion/core/src/datasource/listing/table.rs index 18d84c4ba0c2..08066d14d913 100644 --- a/datafusion/core/src/datasource/listing/table.rs +++ b/datafusion/core/src/datasource/listing/table.rs @@ -1134,7 +1134,7 @@ impl ListingTable { } } -// Expressions can be used for parttion pruning if they can be evaluated using +// Expressions can be used for partition pruning if they can be evaluated using // only the partition columns and there are partition columns. fn can_be_evaluated_for_partition_pruning( partition_column_names: &[&str], diff --git a/datafusion/core/tests/physical_optimizer/filter_pushdown/mod.rs b/datafusion/core/tests/physical_optimizer/filter_pushdown/mod.rs index 41ddfaa100cf..f002355f66e5 100644 --- a/datafusion/core/tests/physical_optimizer/filter_pushdown/mod.rs +++ b/datafusion/core/tests/physical_optimizer/filter_pushdown/mod.rs @@ -1102,7 +1102,7 @@ async fn test_hashjoin_dynamic_filter_pushdown_partitioned() { Arc::new(CoalesceBatchesExec::new(hash_join, 8192)) as Arc; // Top-level CoalescePartitionsExec let cp = Arc::new(CoalescePartitionsExec::new(cb)) as Arc; - // Add a sort for determistic output + // Add a sort for deterministic output let plan = Arc::new(SortExec::new( LexOrdering::new(vec![PhysicalSortExpr::new( col("a", &probe_side_schema).unwrap(), @@ -1303,7 +1303,7 @@ async fn test_hashjoin_dynamic_filter_pushdown_collect_left() { Arc::new(CoalesceBatchesExec::new(hash_join, 8192)) as Arc; // Top-level CoalescePartitionsExec let cp = Arc::new(CoalescePartitionsExec::new(cb)) as Arc; - // Add a sort for determistic output + // Add a sort for deterministic output let plan = Arc::new(SortExec::new( LexOrdering::new(vec![PhysicalSortExpr::new( col("a", &probe_side_schema).unwrap(), diff --git a/datafusion/physical-expr/src/expressions/case.rs b/datafusion/physical-expr/src/expressions/case.rs index 65a210826664..94d49230d31d 100644 --- a/datafusion/physical-expr/src/expressions/case.rs +++ b/datafusion/physical-expr/src/expressions/case.rs @@ -1070,7 +1070,7 @@ mod tests { .into_iter() .collect(); - //let valid_array = vec![true, false, false, true, false, tru + //let valid_array = vec![true, false, false, true, false, true] let null_buffer = Buffer::from([0b00101001u8]); let load4 = load4 .into_data() diff --git a/datafusion/sqllogictest/README.md b/datafusion/sqllogictest/README.md index a389ae1ef60e..58ca3e13b8fd 100644 --- a/datafusion/sqllogictest/README.md +++ b/datafusion/sqllogictest/README.md @@ -349,12 +349,12 @@ query - `test_name`: Uniquely identify the test name (DataFusion only) - `type_string`: A short string that specifies the number of result columns and the expected datatype of each result column. There is one character in the for each result column. The characters codes are: - - 'B' - **B**oolean, - - 'D' - **D**atetime, - - 'I' - **I**nteger, - - 'P' - timestam**P**, + - 'B' - Boolean, + - 'D' - Datetime, + - 'I' - Integer, + - 'P' - timestamP, - 'R' - floating-point results, - - 'T' - **T**ext, + - 'T' - Text, - "?" - any other types - `expected_result`: In the results section, some values are converted according to some rules: - floating point values are rounded to the scale of "12", diff --git a/docs/source/user-guide/introduction.md b/docs/source/user-guide/introduction.md index 51f025d2790c..b721448a6d41 100644 --- a/docs/source/user-guide/introduction.md +++ b/docs/source/user-guide/introduction.md @@ -86,7 +86,7 @@ Here are some example systems built using DataFusion: By using DataFusion, projects are freed to focus on their specific features, and avoid reimplementing general (but still necessary) features such as an expression representation, standard optimizations, -parellelized streaming execution plans, file format support, etc. +parallelized streaming execution plans, file format support, etc. ## Known Users