[MINOR][DOCS] Fix all typos in markdown files of doc and similar patterns in other comments
## What changes were proposed in this pull request? This PR tries to fix all typos in all markdown files under `docs` module, and fixes similar typos in other comments, too. ## How was the this patch tested? manual tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11300 from dongjoon-hyun/minor_fix_typos.
This commit is contained in:
Dongjoon Hyun
Sean Owen
parent
1b144455b6
commit
024482bf51
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@ -1962,7 +1962,7 @@ setMethod("sha2", signature(y = "Column", x = "numeric"),
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#' shiftLeft
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#'
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#' Shift the the given value numBits left. If the given value is a long value, this function
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#' Shift the given value numBits left. If the given value is a long value, this function
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#' will return a long value else it will return an integer value.
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#'
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#' @family math_funcs
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@ -1980,7 +1980,7 @@ setMethod("shiftLeft", signature(y = "Column", x = "numeric"),
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#' shiftRight
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#'
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#' Shift the the given value numBits right. If the given value is a long value, it will return
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#' Shift the given value numBits right. If the given value is a long value, it will return
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#' a long value else it will return an integer value.
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#'
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#' @family math_funcs
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@ -1998,7 +1998,7 @@ setMethod("shiftRight", signature(y = "Column", x = "numeric"),
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#' shiftRightUnsigned
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#'
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#' Unsigned shift the the given value numBits right. If the given value is a long value,
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#' Unsigned shift the given value numBits right. If the given value is a long value,
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#' it will return a long value else it will return an integer value.
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#'
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#' @family math_funcs
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@ -299,7 +299,7 @@ sparkRHive.init <- function(jsc = NULL) {
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#'
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#' @param sc existing spark context
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#' @param groupid the ID to be assigned to job groups
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#' @param description description for the the job group ID
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#' @param description description for the job group ID
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#' @param interruptOnCancel flag to indicate if the job is interrupted on job cancellation
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#' @examples
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#'\dontrun{
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@ -39,7 +39,7 @@ import java.io.OutputStream;
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* Suppose you want to estimate the number of times an element {@code x} has appeared in a data
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* stream so far. With probability {@code delta}, the estimate of this frequency is within the
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* range {@code true frequency <= estimate <= true frequency + eps * N}, where {@code N} is the
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* total count of items have appeared the the data stream so far.
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* total count of items have appeared the data stream so far.
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*
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* Under the cover, a {@link CountMinSketch} is essentially a two-dimensional {@code long} array
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* with depth {@code d} and width {@code w}, where
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@ -120,7 +120,7 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
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* The effective storage level refers to the level that actually specifies BlockManager put
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* behavior, not the level originally specified by the user. This is mainly for forcing a
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* MEMORY_AND_DISK partition to disk if there is not enough room to unroll the partition,
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* while preserving the the original semantics of the RDD as specified by the application.
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* while preserving the original semantics of the RDD as specified by the application.
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*/
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private def putInBlockManager[T](
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key: BlockId,
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@ -76,7 +76,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
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}
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/**
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* Returns an RDD containing only the elements in the the inclusive range `lower` to `upper`.
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* Returns an RDD containing only the elements in the inclusive range `lower` to `upper`.
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* If the RDD has been partitioned using a `RangePartitioner`, then this operation can be
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* performed efficiently by only scanning the partitions that might contain matching elements.
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* Otherwise, a standard `filter` is applied to all partitions.
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@ -655,7 +655,7 @@ class DAGScheduler(
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/**
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* Submit a shuffle map stage to run independently and get a JobWaiter object back. The waiter
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* can be used to block until the the job finishes executing or can be used to cancel the job.
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* can be used to block until the job finishes executing or can be used to cancel the job.
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* This method is used for adaptive query planning, to run map stages and look at statistics
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* about their outputs before submitting downstream stages.
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*
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@ -47,7 +47,7 @@ import org.apache.spark.util.{ResetSystemProperties, Utils}
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/**
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* A collection of tests against the historyserver, including comparing responses from the json
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* metrics api to a set of known "golden files". If new endpoints / parameters are added,
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* cases should be added to this test suite. The expected outcomes can be genered by running
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* cases should be added to this test suite. The expected outcomes can be generated by running
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* the HistoryServerSuite.main. Note that this will blindly generate new expectation files matching
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* the current behavior -- the developer must verify that behavior is correct.
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*
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@ -274,12 +274,12 @@ class HistoryServerSuite extends SparkFunSuite with BeforeAndAfter with Matchers
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implicit val webDriver: WebDriver = new HtmlUnitDriver
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implicit val formats = org.json4s.DefaultFormats
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// this test dir is explictly deleted on successful runs; retained for diagnostics when
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// this test dir is explicitly deleted on successful runs; retained for diagnostics when
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// not
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val logDir = Utils.createDirectory(System.getProperty("java.io.tmpdir", "logs"))
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// a new conf is used with the background thread set and running at its fastest
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// alllowed refresh rate (1Hz)
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// allowed refresh rate (1Hz)
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val myConf = new SparkConf()
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.set("spark.history.fs.logDirectory", logDir.getAbsolutePath)
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.set("spark.eventLog.dir", logDir.getAbsolutePath)
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@ -252,7 +252,7 @@ Nodes in the output layer use softmax function:
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\]`
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The number of nodes `$N$` in the output layer corresponds to the number of classes.
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MLPC employes backpropagation for learning the model. We use logistic loss function for optimization and L-BFGS as optimization routine.
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MLPC employs backpropagation for learning the model. We use logistic loss function for optimization and L-BFGS as optimization routine.
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**Example**
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@ -185,7 +185,7 @@ for more details on the API.
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<div data-lang="python" markdown="1">
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Refer to the [Tokenizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Tokenizer) and
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the the [RegexTokenizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.RegexTokenizer)
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the [RegexTokenizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.RegexTokenizer)
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for more details on the API.
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{% include_example python/ml/tokenizer_example.py %}
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@ -459,7 +459,7 @@ column, we should get the following:
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"a" gets index `0` because it is the most frequent, followed by "c" with index `1` and "b" with
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index `2`.
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Additionaly, there are two strategies regarding how `StringIndexer` will handle
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Additionally, there are two strategies regarding how `StringIndexer` will handle
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unseen labels when you have fit a `StringIndexer` on one dataset and then use it
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to transform another:
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@ -779,7 +779,7 @@ for more details on the API.
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* `splits`: Parameter for mapping continuous features into buckets. With n+1 splits, there are n buckets. A bucket defined by splits x,y holds values in the range [x,y) except the last bucket, which also includes y. Splits should be strictly increasing. Values at -inf, inf must be explicitly provided to cover all Double values; Otherwise, values outside the splits specified will be treated as errors. Two examples of `splits` are `Array(Double.NegativeInfinity, 0.0, 1.0, Double.PositiveInfinity)` and `Array(0.0, 1.0, 2.0)`.
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Note that if you have no idea of the upper bound and lower bound of the targeted column, you would better add the `Double.NegativeInfinity` and `Double.PositiveInfinity` as the bounds of your splits to prevent a potenial out of Bucketizer bounds exception.
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Note that if you have no idea of the upper bound and lower bound of the targeted column, you would better add the `Double.NegativeInfinity` and `Double.PositiveInfinity` as the bounds of your splits to prevent a potential out of Bucketizer bounds exception.
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Note also that the splits that you provided have to be in strictly increasing order, i.e. `s0 < s1 < s2 < ... < sn`.
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@ -628,7 +628,7 @@ Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/
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The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.RegressionEvaluator)
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for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.BinaryClassificationEvaluator)
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for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator)
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for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the `setMetricName`
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for multiclass problems. The default metric used to choose the best `ParamMap` can be overridden by the `setMetricName`
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method in each of these evaluators.
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The `ParamMap` which produces the best evaluation metric (averaged over the `$k$` folds) is selected as the best model.
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@ -300,7 +300,7 @@ for i in range(2):
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## Power iteration clustering (PIC)
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Power iteration clustering (PIC) is a scalable and efficient algorithm for clustering vertices of a
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graph given pairwise similarties as edge properties,
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graph given pairwise similarities as edge properties,
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described in [Lin and Cohen, Power Iteration Clustering](http://www.icml2010.org/papers/387.pdf).
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It computes a pseudo-eigenvector of the normalized affinity matrix of the graph via
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[power iteration](http://en.wikipedia.org/wiki/Power_iteration) and uses it to cluster vertices.
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@ -786,7 +786,7 @@ This example shows how to estimate clusters on streaming data.
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<div data-lang="scala" markdown="1">
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Refer to the [`StreamingKMeans` Scala docs](api/scala/index.html#org.apache.spark.mllib.clustering.StreamingKMeans) for details on the API.
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First we import the neccessary classes.
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First we import the necessary classes.
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{% highlight scala %}
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@ -837,7 +837,7 @@ ssc.awaitTermination()
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<div data-lang="python" markdown="1">
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Refer to the [`StreamingKMeans` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.clustering.StreamingKMeans) for more details on the API.
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First we import the neccessary classes.
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First we import the necessary classes.
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{% highlight python %}
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from pyspark.mllib.linalg import Vectors
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@ -67,7 +67,7 @@ plots (recall, false positive rate) points.
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</thead>
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<tbody>
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<tr>
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<td>Precision (Postive Predictive Value)</td>
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<td>Precision (Positive Predictive Value)</td>
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<td>$PPV=\frac{TP}{TP + FP}$</td>
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</tr>
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<tr>
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@ -360,7 +360,7 @@ $$I_A(x) = \begin{cases}1 & \text{if $x \in A$}, \\ 0 & \text{otherwise}.\end{ca
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**Examples**
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The following code snippets illustrate how to evaluate the performance of a multilabel classifer. The examples
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The following code snippets illustrate how to evaluate the performance of a multilabel classifier. The examples
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use the fake prediction and label data for multilabel classification that is shown below.
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Document predictions:
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@ -558,7 +558,7 @@ variable from a number of independent variables.
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<td>$RMSE = \sqrt{\frac{\sum_{i=0}^{N-1} (\mathbf{y}_i - \hat{\mathbf{y}}_i)^2}{N}}$</td>
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</tr>
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<tr>
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<td>Mean Absoloute Error (MAE)</td>
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<td>Mean Absolute Error (MAE)</td>
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<td>$MAE=\sum_{i=0}^{N-1} \left|\mathbf{y}_i - \hat{\mathbf{y}}_i\right|$</td>
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</tr>
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<tr>
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@ -135,7 +135,7 @@ pattern mining problem.
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included in the results.
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* `maxLocalProjDBSize`: the maximum number of items allowed in a
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prefix-projected database before local iterative processing of the
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projected databse begins. This parameter should be tuned with respect
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projected database begins. This parameter should be tuned with respect
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to the size of your executors.
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**Examples**
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@ -108,7 +108,7 @@ The history server can be configured as follows:
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<td>spark.history.fs.update.interval</td>
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<td>10s</td>
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<td>
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The period at which the the filesystem history provider checks for new or
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The period at which the filesystem history provider checks for new or
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updated logs in the log directory. A shorter interval detects new applications faster,
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at the expense of more server load re-reading updated applications.
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As soon as an update has completed, listings of the completed and incomplete applications
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@ -629,7 +629,7 @@ class MyClass {
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}
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{% endhighlight %}
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is equilvalent to writing `rdd.map(x => this.field + x)`, which references all of `this`. To avoid this
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is equivalent to writing `rdd.map(x => this.field + x)`, which references all of `this`. To avoid this
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issue, the simplest way is to copy `field` into a local variable instead of accessing it externally:
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{% highlight scala %}
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@ -188,7 +188,7 @@ overhead, but at the cost of reserving the Mesos resources for the complete dura
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application.
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Coarse-grained is the default mode. You can also set `spark.mesos.coarse` property to true
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to turn it on explictly in [SparkConf](configuration.html#spark-properties):
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to turn it on explicitly in [SparkConf](configuration.html#spark-properties):
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{% highlight scala %}
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conf.set("spark.mesos.coarse", "true")
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@ -384,7 +384,7 @@ See the [configuration page](configuration.html) for information on Spark config
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<li>Scalar constraints are matched with "less than equal" semantics i.e. value in the constraint must be less than or equal to the value in the resource offer.</li>
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<li>Range constraints are matched with "contains" semantics i.e. value in the constraint must be within the resource offer's value.</li>
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<li>Set constraints are matched with "subset of" semantics i.e. value in the constraint must be a subset of the resource offer's value.</li>
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<li>Text constraints are metched with "equality" semantics i.e. value in the constraint must be exactly equal to the resource offer's value.</li>
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<li>Text constraints are matched with "equality" semantics i.e. value in the constraint must be exactly equal to the resource offer's value.</li>
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<li>In case there is no value present as a part of the constraint any offer with the corresponding attribute will be accepted (without value check).</li>
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</ul>
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</td>
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@ -335,7 +335,7 @@ By default, standalone scheduling clusters are resilient to Worker failures (ins
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**Overview**
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Utilizing ZooKeeper to provide leader election and some state storage, you can launch multiple Masters in your cluster connected to the same ZooKeeper instance. One will be elected "leader" and the others will remain in standby mode. If the current leader dies, another Master will be elected, recover the old Master's state, and then resume scheduling. The entire recovery process (from the time the the first leader goes down) should take between 1 and 2 minutes. Note that this delay only affects scheduling _new_ applications -- applications that were already running during Master failover are unaffected.
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Utilizing ZooKeeper to provide leader election and some state storage, you can launch multiple Masters in your cluster connected to the same ZooKeeper instance. One will be elected "leader" and the others will remain in standby mode. If the current leader dies, another Master will be elected, recover the old Master's state, and then resume scheduling. The entire recovery process (from the time the first leader goes down) should take between 1 and 2 minutes. Note that this delay only affects scheduling _new_ applications -- applications that were already running during Master failover are unaffected.
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Learn more about getting started with ZooKeeper [here](http://zookeeper.apache.org/doc/trunk/zookeeperStarted.html).
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@ -1372,7 +1372,7 @@ Hive metastore Parquet table to a Spark SQL Parquet table. The reconciliation ru
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1. The reconciled schema contains exactly those fields defined in Hive metastore schema.
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- Any fields that only appear in the Parquet schema are dropped in the reconciled schema.
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- Any fileds that only appear in the Hive metastore schema are added as nullable field in the
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- Any fields that only appear in the Hive metastore schema are added as nullable field in the
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reconciled schema.
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#### Metadata Refreshing
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@ -30,7 +30,7 @@ See the [Flume's documentation](https://flume.apache.org/documentation.html) for
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configuring Flume agents.
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#### Configuring Spark Streaming Application
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1. **Linking:** In your SBT/Maven projrect definition, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
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1. **Linking:** In your SBT/Maven project definition, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
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groupId = org.apache.spark
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artifactId = spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}
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@ -95,7 +95,7 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m
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</div>
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</div>
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- `streamingContext`: StreamingContext containg an application name used by Kinesis to tie this Kinesis application to the Kinesis stream
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- `streamingContext`: StreamingContext containing an application name used by Kinesis to tie this Kinesis application to the Kinesis stream
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- `[Kinesis app name]`: The application name that will be used to checkpoint the Kinesis
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sequence numbers in DynamoDB table.
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@ -216,6 +216,6 @@ de-aggregate records during consumption.
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- Checkpointing too frequently will cause excess load on the AWS checkpoint storage layer and may lead to AWS throttling. The provided example handles this throttling with a random-backoff-retry strategy.
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- If no Kinesis checkpoint info exists when the input DStream starts, it will start either from the oldest record available (InitialPositionInStream.TRIM_HORIZON) or from the latest tip (InitialPostitionInStream.LATEST). This is configurable.
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- If no Kinesis checkpoint info exists when the input DStream starts, it will start either from the oldest record available (InitialPositionInStream.TRIM_HORIZON) or from the latest tip (InitialPositionInStream.LATEST). This is configurable.
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- InitialPositionInStream.LATEST could lead to missed records if data is added to the stream while no input DStreams are running (and no checkpoint info is being stored).
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- InitialPositionInStream.TRIM_HORIZON may lead to duplicate processing of records where the impact is dependent on checkpoint frequency and processing idempotency.
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@ -158,7 +158,7 @@ JavaReceiverInputDStream<String> lines = jssc.socketTextStream("localhost", 9999
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{% endhighlight %}
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This `lines` DStream represents the stream of data that will be received from the data
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server. Each record in this stream is a line of text. Then, we want to split the the lines by
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server. Each record in this stream is a line of text. Then, we want to split the lines by
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space into words.
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{% highlight java %}
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@ -266,7 +266,7 @@ class GraphImpl[VD: ClassTag, ED: ClassTag] protected (
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}
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}
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/** Test whether the closure accesses the the attribute with name `attrName`. */
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/** Test whether the closure accesses the attribute with name `attrName`. */
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private def accessesVertexAttr(closure: AnyRef, attrName: String): Boolean = {
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try {
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BytecodeUtils.invokedMethod(closure, classOf[EdgeTriplet[VD, ED]], attrName)
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@ -166,7 +166,7 @@ object GraphGenerators extends Logging {
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}
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/**
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* This method recursively subdivides the the adjacency matrix into quadrants
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* This method recursively subdivides the adjacency matrix into quadrants
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* until it picks a single cell. The naming conventions in this paper match
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* those of the R-MAT paper. There are a power of 2 number of nodes in the graph.
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* The adjacency matrix looks like:
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@ -1301,7 +1301,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
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/**
|
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* Partitioner used by ALS. We requires that getPartition is a projection. That is, for any key k,
|
||||
* we have getPartition(getPartition(k)) = getPartition(k). Since the the default HashPartitioner
|
||||
* we have getPartition(getPartition(k)) = getPartition(k). Since the default HashPartitioner
|
||||
* satisfies this requirement, we simply use a type alias here.
|
||||
*/
|
||||
private[recommendation] type ALSPartitioner = org.apache.spark.HashPartitioner
|
||||
|
|
|
|||
|
|
@ -479,7 +479,7 @@ def round(col, scale=0):
|
|||
|
||||
@since(1.5)
|
||||
def shiftLeft(col, numBits):
|
||||
"""Shift the the given value numBits left.
|
||||
"""Shift the given value numBits left.
|
||||
|
||||
>>> sqlContext.createDataFrame([(21,)], ['a']).select(shiftLeft('a', 1).alias('r')).collect()
|
||||
[Row(r=42)]
|
||||
|
|
@ -490,7 +490,7 @@ def shiftLeft(col, numBits):
|
|||
|
||||
@since(1.5)
|
||||
def shiftRight(col, numBits):
|
||||
"""Shift the the given value numBits right.
|
||||
"""Shift the given value numBits right.
|
||||
|
||||
>>> sqlContext.createDataFrame([(42,)], ['a']).select(shiftRight('a', 1).alias('r')).collect()
|
||||
[Row(r=21)]
|
||||
|
|
@ -502,7 +502,7 @@ def shiftRight(col, numBits):
|
|||
|
||||
@since(1.5)
|
||||
def shiftRightUnsigned(col, numBits):
|
||||
"""Unsigned shift the the given value numBits right.
|
||||
"""Unsigned shift the given value numBits right.
|
||||
|
||||
>>> df = sqlContext.createDataFrame([(-42,)], ['a'])
|
||||
>>> df.select(shiftRightUnsigned('a', 1).alias('r')).collect()
|
||||
|
|
|
|||
|
|
@ -88,7 +88,7 @@ import org.apache.spark.sql.types.IntegerType
|
|||
* this aggregate consists of the original group by clause, all the requested distinct columns
|
||||
* and the group id. Both de-duplication of distinct column and the aggregation of the
|
||||
* non-distinct group take advantage of the fact that we group by the group id (gid) and that we
|
||||
* have nulled out all non-relevant columns for the the given group.
|
||||
* have nulled out all non-relevant columns the given group.
|
||||
* 3. Aggregating the distinct groups and combining this with the results of the non-distinct
|
||||
* aggregation. In this step we use the group id to filter the inputs for the aggregate
|
||||
* functions. The result of the non-distinct group are 'aggregated' by using the first operator,
|
||||
|
|
|
|||
|
|
@ -238,7 +238,7 @@ case class HyperLogLogPlusPlus(
|
|||
diff * diff
|
||||
}
|
||||
|
||||
// Keep moving bounds as long as the the (exclusive) high bound is closer to the estimate than
|
||||
// Keep moving bounds as long as the (exclusive) high bound is closer to the estimate than
|
||||
// the lower (inclusive) bound.
|
||||
var low = math.max(nearestEstimateIndex - K + 1, 0)
|
||||
var high = math.min(low + K, numEstimates)
|
||||
|
|
|
|||
|
|
@ -83,7 +83,7 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
|
|||
}
|
||||
}
|
||||
|
||||
// Evaluate all the the subexpressions.
|
||||
// Evaluate all the subexpressions.
|
||||
val evalSubexpr = ctx.subexprFunctions.mkString("\n")
|
||||
|
||||
val updates = validExpr.zip(index).map {
|
||||
|
|
|
|||
|
|
@ -148,7 +148,7 @@ object RandomDataGenerator {
|
|||
// for "0001-01-01 00:00:00.000000". We need to find a
|
||||
// number that is greater or equals to this number as a valid timestamp value.
|
||||
while (milliseconds < -62135740800000L) {
|
||||
// 253402329599999L is the the number of milliseconds since
|
||||
// 253402329599999L is the number of milliseconds since
|
||||
// January 1, 1970, 00:00:00 GMT for "9999-12-31 23:59:59.999999".
|
||||
milliseconds = rand.nextLong() % 253402329599999L
|
||||
}
|
||||
|
|
@ -163,7 +163,7 @@ object RandomDataGenerator {
|
|||
// for "0001-01-01 00:00:00.000000". We need to find a
|
||||
// number that is greater or equals to this number as a valid timestamp value.
|
||||
while (milliseconds < -62135740800000L) {
|
||||
// 253402329599999L is the the number of milliseconds since
|
||||
// 253402329599999L is the number of milliseconds since
|
||||
// January 1, 1970, 00:00:00 GMT for "9999-12-31 23:59:59.999999".
|
||||
milliseconds = rand.nextLong() % 253402329599999L
|
||||
}
|
||||
|
|
|
|||
|
|
@ -145,7 +145,7 @@ private[sql] abstract class BaseWriterContainer(
|
|||
// If we are appending data to an existing dir, we will only use the output committer
|
||||
// associated with the file output format since it is not safe to use a custom
|
||||
// committer for appending. For example, in S3, direct parquet output committer may
|
||||
// leave partial data in the destination dir when the the appending job fails.
|
||||
// leave partial data in the destination dir when the appending job fails.
|
||||
//
|
||||
// See SPARK-8578 for more details
|
||||
logInfo(
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ import org.apache.spark.sql.execution.{ShuffledRowRDD, SparkPlan}
|
|||
* If this coordinator has made the decision on how to shuffle data, this [[ShuffleExchange]]
|
||||
* will immediately get its corresponding post-shuffle [[ShuffledRowRDD]].
|
||||
* - If this coordinator has not made the decision on how to shuffle data, it will ask those
|
||||
* registered [[ShuffleExchange]]s to submit their pre-shuffle stages. Then, based on the the
|
||||
* registered [[ShuffleExchange]]s to submit their pre-shuffle stages. Then, based on the
|
||||
* size statistics of pre-shuffle partitions, this coordinator will determine the number of
|
||||
* post-shuffle partitions and pack multiple pre-shuffle partitions with continuous indices
|
||||
* to a single post-shuffle partition whenever necessary.
|
||||
|
|
|
|||
|
|
@ -1782,7 +1782,7 @@ object functions extends LegacyFunctions {
|
|||
def round(e: Column, scale: Int): Column = withExpr { Round(e.expr, Literal(scale)) }
|
||||
|
||||
/**
|
||||
* Shift the the given value numBits left. If the given value is a long value, this function
|
||||
* Shift the given value numBits left. If the given value is a long value, this function
|
||||
* will return a long value else it will return an integer value.
|
||||
*
|
||||
* @group math_funcs
|
||||
|
|
@ -1791,7 +1791,7 @@ object functions extends LegacyFunctions {
|
|||
def shiftLeft(e: Column, numBits: Int): Column = withExpr { ShiftLeft(e.expr, lit(numBits).expr) }
|
||||
|
||||
/**
|
||||
* Shift the the given value numBits right. If the given value is a long value, it will return
|
||||
* Shift the given value numBits right. If the given value is a long value, it will return
|
||||
* a long value else it will return an integer value.
|
||||
*
|
||||
* @group math_funcs
|
||||
|
|
@ -1802,7 +1802,7 @@ object functions extends LegacyFunctions {
|
|||
}
|
||||
|
||||
/**
|
||||
* Unsigned shift the the given value numBits right. If the given value is a long value,
|
||||
* Unsigned shift the given value numBits right. If the given value is a long value,
|
||||
* it will return a long value else it will return an integer value.
|
||||
*
|
||||
* @group math_funcs
|
||||
|
|
|
|||
|
|
@ -173,10 +173,10 @@ trait StreamTest extends QueryTest with Timeouts {
|
|||
testStream(stream.toDF())(actions: _*)
|
||||
|
||||
/**
|
||||
* Executes the specified actions on the the given streaming DataFrame and provides helpful
|
||||
* Executes the specified actions on the given streaming DataFrame and provides helpful
|
||||
* error messages in the case of failures or incorrect answers.
|
||||
*
|
||||
* Note that if the stream is not explictly started before an action that requires it to be
|
||||
* Note that if the stream is not explicitly started before an action that requires it to be
|
||||
* running then it will be automatically started before performing any other actions.
|
||||
*/
|
||||
def testStream(stream: DataFrame)(actions: StreamAction*): Unit = {
|
||||
|
|
|
|||
|
|
@ -721,13 +721,13 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
|
|||
}
|
||||
|
||||
/**
|
||||
* String to scan for when looking for the the thrift binary endpoint running.
|
||||
* String to scan for when looking for the thrift binary endpoint running.
|
||||
* This can change across Hive versions.
|
||||
*/
|
||||
val THRIFT_BINARY_SERVICE_LIVE = "Starting ThriftBinaryCLIService on port"
|
||||
|
||||
/**
|
||||
* String to scan for when looking for the the thrift HTTP endpoint running.
|
||||
* String to scan for when looking for the thrift HTTP endpoint running.
|
||||
* This can change across Hive versions.
|
||||
*/
|
||||
val THRIFT_HTTP_SERVICE_LIVE = "Started ThriftHttpCLIService in http"
|
||||
|
|
|
|||
|
|
@ -364,7 +364,7 @@ private[streaming] object OpenHashMapBasedStateMap {
|
|||
}
|
||||
|
||||
/**
|
||||
* Internal class to represent a marker the demarkate the the end of all state data in the
|
||||
* Internal class to represent a marker the demarkate the end of all state data in the
|
||||
* serialized bytes.
|
||||
*/
|
||||
class LimitMarker(val num: Int) extends Serializable
|
||||
|
|
|
|||
|
|
@ -151,7 +151,7 @@ private[yarn] class AMDelegationTokenRenewer(
|
|||
// passed in already has tokens for that FS even if the tokens are expired (it really only
|
||||
// checks if there are tokens for the service, and not if they are valid). So the only real
|
||||
// way to get new tokens is to make sure a different Credentials object is used each time to
|
||||
// get new tokens and then the new tokens are copied over the the current user's Credentials.
|
||||
// get new tokens and then the new tokens are copied over the current user's Credentials.
|
||||
// So:
|
||||
// - we login as a different user and get the UGI
|
||||
// - use that UGI to get the tokens (see doAs block below)
|
||||
|
|
|
|||
Loading…
Reference in a new issue