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Posted to commits@flink.apache.org by fh...@apache.org on 2016/05/17 22:49:30 UTC
[5/5] flink git commit: [FLINK-3913] [docs] Clean up spelling
mistakes.
[FLINK-3913] [docs] Clean up spelling mistakes.
This closes #1992
Project: http://git-wip-us.apache.org/repos/asf/flink/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/4fbd8eb9
Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/4fbd8eb9
Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/4fbd8eb9
Branch: refs/heads/master
Commit: 4fbd8eb938cf0f1deee550161cc54f27757411e8
Parents: 1e9fff4
Author: Mark Reddy <ma...@gmail.com>
Authored: Mon May 16 00:52:53 2016 +0100
Committer: Fabian Hueske <fh...@apache.org>
Committed: Wed May 18 00:12:22 2016 +0200
----------------------------------------------------------------------
docs/apis/batch/dataset_transformations.md | 4 ++--
docs/apis/batch/examples.md | 6 +++---
docs/apis/batch/index.md | 16 ++++++++--------
docs/apis/batch/iterations.md | 2 +-
docs/apis/batch/libs/gelly.md | 4 ++--
.../batch/libs/ml/multiple_linear_regression.md | 2 +-
docs/apis/batch/libs/ml/pipelines.md | 2 +-
docs/apis/batch/python.md | 2 +-
docs/apis/best_practices.md | 2 +-
docs/apis/common/index.md | 4 ++--
docs/apis/scala_shell.md | 2 +-
docs/apis/streaming/connectors/rabbitmq.md | 2 +-
docs/apis/streaming/event_time.md | 2 +-
.../streaming/event_timestamps_watermarks.md | 2 +-
docs/apis/streaming/fault_tolerance.md | 2 +-
docs/apis/streaming/index.md | 20 ++++++++++----------
docs/apis/streaming/storm_compatibility.md | 4 ++--
docs/apis/table.md | 6 +++---
docs/internals/back_pressure_monitoring.md | 2 +-
docs/internals/general_arch.md | 2 +-
docs/internals/job_scheduling.md | 4 ++--
docs/internals/monitoring_rest_api.md | 6 +++---
docs/setup/config.md | 8 ++++----
docs/setup/local_setup.md | 2 +-
docs/setup/yarn_setup.md | 8 ++++----
25 files changed, 58 insertions(+), 58 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/batch/dataset_transformations.md
----------------------------------------------------------------------
diff --git a/docs/apis/batch/dataset_transformations.md b/docs/apis/batch/dataset_transformations.md
index 2d72817..0de771a 100644
--- a/docs/apis/batch/dataset_transformations.md
+++ b/docs/apis/batch/dataset_transformations.md
@@ -1514,7 +1514,7 @@ The following hints are available:
partitioned) and builds a hash table from the first input. This strategy is good if the first
input is smaller than the second, but both inputs are still large.
*Note:* This is the default fallback strategy that the system uses if no size estimates can be made
- and no pre-existing partitiongs and sort-orders can be re-used.
+ and no pre-existing partitions and sort-orders can be re-used.
* `REPARTITION_HASH_SECOND`: The system partitions (shuffles) each input (unless the input is already
partitioned) and builds a hash table from the second input. This strategy is good if the second
@@ -1528,7 +1528,7 @@ The following hints are available:
### OuterJoin
-The OuterJoin transformation performs a left, right, or full outer join on two data sets. Outer joins are similar to regular (inner) joins and create all pairs of elements that are equal on their keys. In addition, records of the "outer" side (left, right, or both in case of full) are preserved if no matching key is found in the other side. Matching pair of elements (or one element and a `null` value for the other input) are given to a `JoinFunction` to turn the pair of elements into a single element, or to a `FlatJoinFunction` to turn the pair of elements into arbitararily many (including none) elements.
+The OuterJoin transformation performs a left, right, or full outer join on two data sets. Outer joins are similar to regular (inner) joins and create all pairs of elements that are equal on their keys. In addition, records of the "outer" side (left, right, or both in case of full) are preserved if no matching key is found in the other side. Matching pair of elements (or one element and a `null` value for the other input) are given to a `JoinFunction` to turn the pair of elements into a single element, or to a `FlatJoinFunction` to turn the pair of elements into arbitrarily many (including none) elements.
The elements of both DataSets are joined on one or more keys which can be specified using
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/batch/examples.md
----------------------------------------------------------------------
diff --git a/docs/apis/batch/examples.md b/docs/apis/batch/examples.md
index 3b6ccd4..3523185 100644
--- a/docs/apis/batch/examples.md
+++ b/docs/apis/batch/examples.md
@@ -118,7 +118,7 @@ val counts = text.flatMap { _.toLowerCase.split("\\W+") filter { _.nonEmpty } }
counts.writeAsCsv(outputPath, "\n", " ")
~~~
-The {% gh_link /flink-examples/flink-exampls-batch/src/main/scala/org/apache/flink/examples/scala/wordcount/WordCount.scala "WordCount example" %} implements the above described algorithm with input parameters: `--input <path> --output <path>`. As test data, any text file will do.
+The {% gh_link /flink-examples/flink-examples-batch/src/main/scala/org/apache/flink/examples/scala/wordcount/WordCount.scala "WordCount example" %} implements the above described algorithm with input parameters: `--input <path> --output <path>`. As test data, any text file will do.
</div>
@@ -171,11 +171,11 @@ public static final class JoinVertexWithEdgesMatch
@Override
public void join(<Tuple2<Long, Double> page, Tuple2<Long, Long[]> adj,
Collector<Tuple2<Long, Double>> out) {
- Long[] neigbors = adj.f1;
+ Long[] neighbors = adj.f1;
double rank = page.f1;
double rankToDistribute = rank / ((double) neigbors.length);
- for (int i = 0; i < neigbors.length; i++) {
+ for (int i = 0; i < neighbors.length; i++) {
out.collect(new Tuple2<Long, Double>(neighbors[i], rankToDistribute));
}
}
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/batch/index.md
----------------------------------------------------------------------
diff --git a/docs/apis/batch/index.md b/docs/apis/batch/index.md
index 653e5d7..4c0aea8 100644
--- a/docs/apis/batch/index.md
+++ b/docs/apis/batch/index.md
@@ -348,7 +348,7 @@ DataSet<Integer> data1 = // [...]
DataSet<String> data2 = // [...]
DataSet<Tuple2<Integer, String>> result = data1.cross(data2);
{% endhighlight %}
- <p>Note: Cross is potentially a <b>very</b> compute-intensive operation which can challenge even large compute clusters! It is adviced to hint the system with the DataSet sizes by using <i>crossWithTiny()</i> and <i>crossWithHuge()</i>.</p>
+ <p>Note: Cross is potentially a <b>very</b> compute-intensive operation which can challenge even large compute clusters! It is advised to hint the system with the DataSet sizes by using <i>crossWithTiny()</i> and <i>crossWithHuge()</i>.</p>
</td>
</tr>
<tr>
@@ -560,7 +560,7 @@ val output: DataSet[(Int, String, Doublr)] = input.aggregate(SUM, 0).aggregate(M
<p>You can also use short-hand syntax for minimum, maximum, and sum aggregations.</p>
{% highlight scala %}
val input: DataSet[(Int, String, Double)] = // [...]
-val output: DataSet[(Int, String, Doublr)] = input.sum(0).min(2)
+val output: DataSet[(Int, String, Double)] = input.sum(0).min(2)
{% endhighlight %}
</td>
</tr>
@@ -1100,7 +1100,7 @@ DataSet:
- `writeAsCsv(...)` / `CsvOutputFormat` - Writes tuples as comma-separated value files. Row and field
delimiters are configurable. The value for each field comes from the *toString()* method of the objects.
- `print()` / `printToErr()` / `print(String msg)` / `printToErr(String msg)` - Prints the *toString()* value
-of each element on the standard out / strandard error stream. Optionally, a prefix (msg) can be provided which is
+of each element on the standard out / standard error stream. Optionally, a prefix (msg) can be provided which is
prepended to the output. This can help to distinguish between different calls to *print*. If the parallelism is
greater than 1, the output will also be prepended with the identifier of the task which produced the output.
- `write()` / `FileOutputFormat` - Method and base class for custom file outputs. Supports
@@ -1135,7 +1135,7 @@ values.writeAsCsv("file:///path/to/the/result/file", "\n", "|");
// this writes tuples in the text formatting "(a, b, c)", rather than as CSV lines
values.writeAsText("file:///path/to/the/result/file");
-// this wites values as strings using a user-defined TextFormatter object
+// this writes values as strings using a user-defined TextFormatter object
values.writeAsFormattedText("file:///path/to/the/result/file",
new TextFormatter<Tuple2<Integer, Integer>>() {
public String format (Tuple2<Integer, Integer> value) {
@@ -1199,12 +1199,12 @@ using an
Flink comes with a variety of built-in output formats that are encapsulated behind operations on the
DataSet:
-- `writeAsText()` / `TextOuputFormat` - Writes elements line-wise as Strings. The Strings are
+- `writeAsText()` / `TextOutputFormat` - Writes elements line-wise as Strings. The Strings are
obtained by calling the *toString()* method of each element.
- `writeAsCsv(...)` / `CsvOutputFormat` - Writes tuples as comma-separated value files. Row and field
delimiters are configurable. The value for each field comes from the *toString()* method of the objects.
- `print()` / `printToErr()` - Prints the *toString()* value of each element on the
- standard out / strandard error stream.
+ standard out / standard error stream.
- `write()` / `FileOutputFormat` - Method and base class for custom file outputs. Supports
custom object-to-bytes conversion.
- `output()`/ `OutputFormat` - Most generic output method, for data sinks that are not file based
@@ -1237,7 +1237,7 @@ values.writeAsCsv("file:///path/to/the/result/file", "\n", "|")
// this writes tuples in the text formatting "(a, b, c)", rather than as CSV lines
values.writeAsText("file:///path/to/the/result/file");
-// this wites values as strings using a user-defined formatting
+// this writes values as strings using a user-defined formatting
values map { tuple => tuple._1 + " - " + tuple._2 }
.writeAsText("file:///path/to/the/result/file")
{% endhighlight %}
@@ -1616,7 +1616,7 @@ result data. This section give some hints how to ease the development of Flink p
### Local Execution Environment
A `LocalEnvironment` starts a Flink system within the same JVM process it was created in. If you
-start the LocalEnvironement from an IDE, you can set breakpoints in your code and easily debug your
+start the LocalEnvironment from an IDE, you can set breakpoints in your code and easily debug your
program.
A LocalEnvironment is created and used as follows:
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/batch/iterations.md
----------------------------------------------------------------------
diff --git a/docs/apis/batch/iterations.md b/docs/apis/batch/iterations.md
index 912f378..bad058f 100644
--- a/docs/apis/batch/iterations.md
+++ b/docs/apis/batch/iterations.md
@@ -191,7 +191,7 @@ In the following example, every vertex has an **ID** and a **coloring**. Each ve
<img alt="Delta Iterate Operator Example" width="100%" src="fig/iterations_delta_iterate_operator_example.png" />
</p>
-The **intial input** is set as **both workset and solution set.** In the above figure, the colors visualize the **evolution of the solution set**. With each iteration, the color of the minimum ID is spreading in the respective subgraph. At the same time, the amount of work (exchanged and compared vertex IDs) decreases with each iteration. This corresponds to the **decreasing size of the workset**, which goes from all seven vertices to zero after three iterations, at which time the iteration terminates. The **important observation** is that *the lower subgraph converges before the upper half* does and the delta iteration is able to capture this with the workset abstraction.
+The **initial input** is set as **both workset and solution set.** In the above figure, the colors visualize the **evolution of the solution set**. With each iteration, the color of the minimum ID is spreading in the respective subgraph. At the same time, the amount of work (exchanged and compared vertex IDs) decreases with each iteration. This corresponds to the **decreasing size of the workset**, which goes from all seven vertices to zero after three iterations, at which time the iteration terminates. The **important observation** is that *the lower subgraph converges before the upper half* does and the delta iteration is able to capture this with the workset abstraction.
In the upper subgraph **ID 1** (*orange*) is the **minimum ID**. In the **first iteration**, it will get propagated to vertex 2, which will subsequently change its color to orange. Vertices 3 and 4 will receive **ID 2** (in *yellow*) as their current minimum ID and change to yellow. Because the color of *vertex 1* didn't change in the first iteration, it can be skipped it in the next workset.
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/batch/libs/gelly.md
----------------------------------------------------------------------
diff --git a/docs/apis/batch/libs/gelly.md b/docs/apis/batch/libs/gelly.md
index dd0b4c1..5d9bfd6 100644
--- a/docs/apis/batch/libs/gelly.md
+++ b/docs/apis/batch/libs/gelly.md
@@ -1957,7 +1957,7 @@ PageRank is an algorithm that was first used to rank web search engine results.
#### Details
The algorithm operates in iterations, where pages distribute their scores to their neighbors (pages they have links to) and subsequently update their scores based on the partial values they receive. The implementation assumes that each page has at least one incoming and one outgoing link.
-In order to consider the importance of a link from one page to another, scores are divided by the total number of out-links of the source page. Thus, a page with 10 links will distribute 1/10 of its score to each neighbor, while a page with 100 links, will distribute 1/100 of its score to each neighboring page. This process computes what is often called the transition probablities, i.e. the probability that some page will lead to other page while surfing the web. To correctly compute the transition probabilities, this implementation expectes the edge values to be initialiez to 1.0.
+In order to consider the importance of a link from one page to another, scores are divided by the total number of out-links of the source page. Thus, a page with 10 links will distribute 1/10 of its score to each neighbor, while a page with 100 links, will distribute 1/100 of its score to each neighboring page. This process computes what is often called the transition probablities, i.e. the probability that some page will lead to other page while surfing the web. To correctly compute the transition probabilities, this implementation expects the edge values to be initialised to 1.0.
#### Usage
The algorithm takes as input a `Graph` with any vertex type, `Double` vertex values, and `Double` edge values. Edges values should be initialized to 1.0, in order to correctly compute the transition probabilities. Otherwise, the transition probability for an Edge `(u, v)` will be set to the edge value divided by `u`'s out-degree. The algorithm returns a `DataSet` of vertices, where the vertex value corresponds to assigned rank after convergence (or maximum iterations).
@@ -2679,7 +2679,7 @@ A directed or undirected power-law graph generated using the
[Recursive Matrix (R-Mat)](http://www.cs.cmu.edu/~christos/PUBLICATIONS/siam04.pdf) model.
RMat is a stochastic generator configured with a source of randomness implementing the
-`RandomGenerableFactory` interface. Provided implemenations are `JDKRandomGeneratorFactory`
+`RandomGenerableFactory` interface. Provided implementations are `JDKRandomGeneratorFactory`
and `MersenneTwisterFactory`. These generate an initial sequence of random values which are
then used as seeds for generating the edges.
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/batch/libs/ml/multiple_linear_regression.md
----------------------------------------------------------------------
diff --git a/docs/apis/batch/libs/ml/multiple_linear_regression.md b/docs/apis/batch/libs/ml/multiple_linear_regression.md
index 436f95b..b427eac 100644
--- a/docs/apis/batch/libs/ml/multiple_linear_regression.md
+++ b/docs/apis/batch/libs/ml/multiple_linear_regression.md
@@ -135,7 +135,7 @@ the algorithm's performance.
<td>
<p>
Learning rate method used to calculate the effective learning rate for each iteration.
- See the list of supported <a href="optimization.html">learing rate methods</a>.
+ See the list of supported <a href="optimization.html">learning rate methods</a>.
(Default value: <strong>LearningRateMethod.Default</strong>)
</p>
</td>
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/batch/libs/ml/pipelines.md
----------------------------------------------------------------------
diff --git a/docs/apis/batch/libs/ml/pipelines.md b/docs/apis/batch/libs/ml/pipelines.md
index a6cce45..f86476c 100644
--- a/docs/apis/batch/libs/ml/pipelines.md
+++ b/docs/apis/batch/libs/ml/pipelines.md
@@ -403,7 +403,7 @@ Furthermore, we can now use this transformer as part of an analysis pipeline whe
val trainingData: DataSet[DenseVector] = ...
val mean = MeanTransformer.setMean(1.0)
-val polyFeaturs = PolynomialFeatures().setDegree(3)
+val polyFeatures = PolynomialFeatures().setDegree(3)
val pipeline = mean.chainTransformer(polyFeatures)
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/batch/python.md
----------------------------------------------------------------------
diff --git a/docs/apis/batch/python.md b/docs/apis/batch/python.md
index 646683d..8771cb5 100644
--- a/docs/apis/batch/python.md
+++ b/docs/apis/batch/python.md
@@ -563,7 +563,7 @@ The degree of parallelism of a task can be specified in Flink on different level
### Execution Environment Level
-Flink programs are executed in the context of an [execution environmentt](#program-skeleton). An
+Flink programs are executed in the context of an [execution environment](#program-skeleton). An
execution environment defines a default parallelism for all operators, data sources, and data sinks
it executes. Execution environment parallelism can be overwritten by explicitly configuring the
parallelism of an operator.
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/best_practices.md
----------------------------------------------------------------------
diff --git a/docs/apis/best_practices.md b/docs/apis/best_practices.md
index 4d911dd..62e0ebf 100644
--- a/docs/apis/best_practices.md
+++ b/docs/apis/best_practices.md
@@ -214,7 +214,7 @@ final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
env.getConfig().registerTypeWithKryoSerializer(MyCustomType.class, ProtobufSerializer.class);
// register the serializer included with Apache Thrift as the standard serializer
-// TBaseSerializer states it should be initalized as a default Kryo serializer
+// TBaseSerializer states it should be initialized as a default Kryo serializer
env.getConfig().addDefaultKryoSerializer(MyCustomType.class, TBaseSerializer.class);
{% endhighlight %}
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/common/index.md
----------------------------------------------------------------------
diff --git a/docs/apis/common/index.md b/docs/apis/common/index.md
index cb2c78d..022f961 100644
--- a/docs/apis/common/index.md
+++ b/docs/apis/common/index.md
@@ -336,7 +336,7 @@ print()
</div>
Once you specified the complete program you need to **trigger the program execution** by calling
-`execute()` on the `StreamExecutionEnviroment`.
+`execute()` on the `StreamExecutionEnvironment`.
Depending on the type of the `ExecutionEnvironment` the execution will be triggered on your local
machine or submit your program for execution on a cluster.
@@ -1236,7 +1236,7 @@ Client can either be a Java or a Scala program. One example of such a Client is
Flink's Command-line Interface (CLI).
For the CLI client, the parallelism parameter can be specified with `-p`. For
-exampple:
+example:
./bin/flink run -p 10 ../examples/*WordCount-java*.jar
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/scala_shell.md
----------------------------------------------------------------------
diff --git a/docs/apis/scala_shell.md b/docs/apis/scala_shell.md
index 0377f5a..ad36ca0 100644
--- a/docs/apis/scala_shell.md
+++ b/docs/apis/scala_shell.md
@@ -62,7 +62,7 @@ Scala-Flink> counts.print()
The print() command will automatically send the specified tasks to the JobManager for execution and will show the result of the computation in the terminal.
-It is possbile to write results to a file. However, in this case you need to call `execute`, to run your program:
+It is possible to write results to a file. However, in this case you need to call `execute`, to run your program:
~~~scala
Scala-Flink> benv.execute("MyProgram")
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/streaming/connectors/rabbitmq.md
----------------------------------------------------------------------
diff --git a/docs/apis/streaming/connectors/rabbitmq.md b/docs/apis/streaming/connectors/rabbitmq.md
index f5ea702..b48608d 100644
--- a/docs/apis/streaming/connectors/rabbitmq.md
+++ b/docs/apis/streaming/connectors/rabbitmq.md
@@ -50,7 +50,7 @@ The followings have to be provided for the `RMQSource(\u2026)` constructor in order
- hostName: The RabbitMQ broker hostname.
- queueName: The RabbitMQ queue name.
- usesCorrelationId: `true` when correlation ids should be used, `false` otherwise (default is `false`).
-- deserializationScehma: Deserialization schema to turn messages into Java objects.
+- deserializationSchema: Deserialization schema to turn messages into Java objects.
This source can be operated in three different modes:
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/streaming/event_time.md
----------------------------------------------------------------------
diff --git a/docs/apis/streaming/event_time.md b/docs/apis/streaming/event_time.md
index 0b4da03..7bc53e7 100644
--- a/docs/apis/streaming/event_time.md
+++ b/docs/apis/streaming/event_time.md
@@ -84,7 +84,7 @@ Flink supports different notions of *time* in streaming programs.
The first part of a Flink DataStream program is usually to set the base *time characteristic*. That setting
defines how data stream sources behave (for example whether to assign timestamps), and what notion of
-time the window operations like `KeyedStream.timeWindow(Time.secondss(30))` refer to.
+time the window operations like `KeyedStream.timeWindow(Time.seconds(30))` refer to.
The following example shows a Flink program that aggregates events in hourly time windows. The behavior of the
windows adapts with the time characteristic.
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/streaming/event_timestamps_watermarks.md
----------------------------------------------------------------------
diff --git a/docs/apis/streaming/event_timestamps_watermarks.md b/docs/apis/streaming/event_timestamps_watermarks.md
index 11c97a7..0ec2810 100644
--- a/docs/apis/streaming/event_timestamps_watermarks.md
+++ b/docs/apis/streaming/event_timestamps_watermarks.md
@@ -69,7 +69,7 @@ There are two ways to assign timestamps and generate Watermarks:
Stream sources can also directly assign timestamps to the elements they produce and emit Watermarks. In that case,
no Timestamp Assigner is needed.
-To assign a timestamp to an element in the soruce directly, the source must use the `collectWithTimestamp(...)`
+To assign a timestamp to an element in the source directly, the source must use the `collectWithTimestamp(...)`
method on the `SourceContext`. To generate Watermarks, the source must call the `emitWatermark(Watermark)` function.
Below is a simple example of a source *(non-checkpointed)* that assigns timestamps and generates Watermarks
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/streaming/fault_tolerance.md
----------------------------------------------------------------------
diff --git a/docs/apis/streaming/fault_tolerance.md b/docs/apis/streaming/fault_tolerance.md
index d85038b..f6e90a4 100644
--- a/docs/apis/streaming/fault_tolerance.md
+++ b/docs/apis/streaming/fault_tolerance.md
@@ -36,7 +36,7 @@ transient program failures, etc.
Streaming Fault Tolerance
-------------------------
-Flink has a checkpointing mechanism that recovers streaming jobs after failues. The checkpointing mechanism requires a *persistent* (or *durable*) source that
+Flink has a checkpointing mechanism that recovers streaming jobs after failures. The checkpointing mechanism requires a *persistent* (or *durable*) source that
can be asked for prior records again (Apache Kafka is a good example of such a source).
The checkpointing mechanism stores the progress in the data sources and data sinks, the state of windows, as well as the user-defined state (see [Working with State](state.html)) consistently to provide *exactly once* processing semantics. Where the checkpoints are stored (e.g., JobManager memory, file system, database) depends on the configured [state backend](state_backends.html).
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/streaming/index.md
----------------------------------------------------------------------
diff --git a/docs/apis/streaming/index.md b/docs/apis/streaming/index.md
index 17d8b56..8a825dd 100644
--- a/docs/apis/streaming/index.md
+++ b/docs/apis/streaming/index.md
@@ -270,7 +270,7 @@ DataStream<String> result =
<td><strong>Aggregations</strong><br>KeyedStream → DataStream</td>
<td>
<p>Rolling aggregations on a keyed data stream. The difference between min
- and minBy is that min returns the minimun value, whereas minBy returns
+ and minBy is that min returns the minimum value, whereas minBy returns
the element that has the minimum value in this field (same for max and maxBy).</p>
{% highlight java %}
keyedStream.sum(0);
@@ -726,7 +726,7 @@ val result: DataStream[String] =
<td><strong>Aggregations on windows</strong><br>WindowedStream → DataStream</td>
<td>
<p>Aggregates the contents of a window. The difference between min
- and minBy is that min returns the minimun value, whereas minBy returns
+ and minBy is that min returns the minimum value, whereas minBy returns
the element that has the minimum value in this field (same for max and maxBy).</p>
{% highlight scala %}
windowedStream.sum(0)
@@ -1415,14 +1415,14 @@ Data sinks consume DataStreams and forward them to files, sockets, external syst
Flink comes with a variety of built-in output formats that are encapsulated behind operations on the
DataStreams:
-- `writeAsText()` / `TextOuputFormat` - Writes elements line-wise as Strings. The Strings are
+- `writeAsText()` / `TextOutputFormat` - Writes elements line-wise as Strings. The Strings are
obtained by calling the *toString()* method of each element.
- `writeAsCsv(...)` / `CsvOutputFormat` - Writes tuples as comma-separated value files. Row and field
delimiters are configurable. The value for each field comes from the *toString()* method of the objects.
- `print()` / `printToErr()` - Prints the *toString()* value
-of each element on the standard out / strandard error stream. Optionally, a prefix (msg) can be provided which is
+of each element on the standard out / standard error stream. Optionally, a prefix (msg) can be provided which is
prepended to the output. This can help to distinguish between different calls to *print*. If the parallelism is
greater than 1, the output will also be prepended with the identifier of the task which produced the output.
@@ -1443,14 +1443,14 @@ Data sinks consume DataStreams and forward them to files, sockets, external syst
Flink comes with a variety of built-in output formats that are encapsulated behind operations on the
DataStreams:
-- `writeAsText()` / `TextOuputFormat` - Writes elements line-wise as Strings. The Strings are
+- `writeAsText()` / `TextOutputFormat` - Writes elements line-wise as Strings. The Strings are
obtained by calling the *toString()* method of each element.
- `writeAsCsv(...)` / `CsvOutputFormat` - Writes tuples as comma-separated value files. Row and field
delimiters are configurable. The value for each field comes from the *toString()* method of the objects.
- `print()` / `printToErr()` - Prints the *toString()* value
-of each element on the standard out / strandard error stream. Optionally, a prefix (msg) can be provided which is
+of each element on the standard out / standard error stream. Optionally, a prefix (msg) can be provided which is
prepended to the output. This can help to distinguish between different calls to *print*. If the parallelism is
greater than 1, the output will also be prepended with the identifier of the task which produced the output.
@@ -1472,7 +1472,7 @@ OutputFormat. This means that not all elements send to the OutputFormat are imme
in the target system. Also, in failure cases, those records might be lost.
For reliable, exactly-once delivery of a stream into a file system, use the `flink-connector-filesystem`.
-Also, custom implementations through the `.addSink(...)` method can partiticpate in Flink's checkpointing
+Also, custom implementations through the `.addSink(...)` method can participate in Flink's checkpointing
for exactly-once semantics.
{% top %}
@@ -1494,7 +1494,7 @@ or a `filter`. Here, we show an example using filters. First, we define an `Iter
IterativeStream<Integer> iteration = input.iterate();
{% endhighlight %}
-Then, we specify the logic that will be executed inside the loop using a series of trasformations (here
+Then, we specify the logic that will be executed inside the loop using a series of transformations (here
a simple `map` transformation)
{% highlight java %}
@@ -1627,7 +1627,7 @@ Usage:
LocalStreamEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();
env.setBufferTimeout(timeoutMillis);
-env.genereateSequence(1,10).map(new MyMapper()).setBufferTimeout(timeoutMillis);
+env.generateSequence(1,10).map(new MyMapper()).setBufferTimeout(timeoutMillis);
{% endhighlight %}
</div>
<div data-lang="scala" markdown="1">
@@ -1660,7 +1660,7 @@ result data. This section give some hints how to ease the development of Flink p
### Local Execution Environment
A `LocalStreamEnvironment` starts a Flink system within the same JVM process it was created in. If you
-start the LocalEnvironement from an IDE, you can set breakpoints in your code and easily debug your
+start the LocalEnvironment from an IDE, you can set breakpoints in your code and easily debug your
program.
A LocalEnvironment is created and used as follows:
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/streaming/storm_compatibility.md
----------------------------------------------------------------------
diff --git a/docs/apis/streaming/storm_compatibility.md b/docs/apis/streaming/storm_compatibility.md
index 6c8c018..94e0042 100644
--- a/docs/apis/streaming/storm_compatibility.md
+++ b/docs/apis/streaming/storm_compatibility.md
@@ -166,7 +166,7 @@ Bolts can accesses input tuple fields via name (additionally to access via index
To use this feature with embedded Bolts, you need to have either a
1. [POJO]({{site.baseurl}}/apis/batch/index.html#pojos) type input stream or
- 2. [Tuple]({{site.baseurl}}/apis/batch/index.html#tuples-and-case-classes) type input stream and spedify the input schema (ie, name-to-index-mapping)
+ 2. [Tuple]({{site.baseurl}}/apis/batch/index.html#tuples-and-case-classes) type input stream and specify the input schema (i.e. name-to-index-mapping)
For POJO input types, Flink accesses the fields via reflection.
For this case, Flink expects either a corresponding public member variable or public getter method.
@@ -261,7 +261,7 @@ An example of a finite Spout that emits records for 10 seconds only:
<div data-lang="java" markdown="1">
~~~java
public class TimedFiniteSpout extends BaseRichSpout implements FiniteSpout {
- [...] // implemente open(), nextTuple(), ...
+ [...] // implement open(), nextTuple(), ...
private long starttime = System.currentTimeMillis();
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/apis/table.md
----------------------------------------------------------------------
diff --git a/docs/apis/table.md b/docs/apis/table.md
index 3defdbb..eae4ebd 100644
--- a/docs/apis/table.md
+++ b/docs/apis/table.md
@@ -29,7 +29,7 @@ under the License.
**Table API and SQL are experimental features**
The Table API is a SQL-like expression language for relational stream and batch processing that can be easily embedded in Flink's DataSet and DataStream APIs (Java and Scala).
-The Table API and SQL insterface operate on a relational `Table` abstraction, which can be created from external data sources, or existing DataSets and DataStreams. With the Table API, you can apply relational operators such as selection, aggregation, and joins on `Table`s.
+The Table API and SQL interface operate on a relational `Table` abstraction, which can be created from external data sources, or existing DataSets and DataStreams. With the Table API, you can apply relational operators such as selection, aggregation, and joins on `Table`s.
`Table`s can also be queried with regular SQL, as long as they are registered (see [Registering and Accessing Tables](#registering-and-accessing-tables)). The Table API and SQL offer equivalent functionality and can be mixed in the same program. When a `Table` is converted back into a `DataSet` or `DataStream`, the logical plan, which was defined by relational operators and SQL queries, is optimized using [Apache Calcite](https://calcite.apache.org/) and transformed into a `DataSet` or `DataStream` execution plan.
@@ -130,7 +130,7 @@ tableEnv.registerDataStream("Orders", ord, 'user, 'product, 'amount)
### Register a Table
-A `Table` that originates from a Table API operation or a SQL query is registered in a `TableEnvironemnt` as follows:
+A `Table` that originates from a Table API operation or a SQL query is registered in a `TableEnvironment` as follows:
<div class="codetabs" markdown="1">
<div data-lang="java" markdown="1">
@@ -688,7 +688,7 @@ SQL queries can be executed on DataStream Tables by adding the `STREAM` SQL keyw
Emit a Table to external sinks
----
-A `Table` can be emitted to a `TableSink`, which is a generic interface to support a wide variaty of file formats (e.g., CSV, Apache Parquet, Apache Avro), storage systems (e.g., JDBC, Apache HBase, Apache Cassandra, Elasticsearch), or messaging systems (e.g., Apache Kafka, RabbitMQ), and others. A batch `Table` can only be emitted by a `BatchTableSink`, a streaming table requires a `StreamTableSink` (a `TableSink` can implement both interfaces).
+A `Table` can be emitted to a `TableSink`, which is a generic interface to support a wide variety of file formats (e.g. CSV, Apache Parquet, Apache Avro), storage systems (e.g., JDBC, Apache HBase, Apache Cassandra, Elasticsearch), or messaging systems (e.g., Apache Kafka, RabbitMQ), and others. A batch `Table` can only be emitted by a `BatchTableSink`, a streaming table requires a `StreamTableSink` (a `TableSink` can implement both interfaces).
Currently, Flink only provides a `CsvTableSink` that writes a batch or streaming `Table` to CSV-formatted files. A custom `TableSource` can be defined by implementing the `BatchTableSink` and/or `StreamTableSink` interface.
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/internals/back_pressure_monitoring.md
----------------------------------------------------------------------
diff --git a/docs/internals/back_pressure_monitoring.md b/docs/internals/back_pressure_monitoring.md
index d272eaf..ee25e3b 100644
--- a/docs/internals/back_pressure_monitoring.md
+++ b/docs/internals/back_pressure_monitoring.md
@@ -54,7 +54,7 @@ In order to not overload the task managers with stack trace samples, the web int
## Configuration
-You can configure the number of samples for the job manager with the following confiugration keys:
+You can configure the number of samples for the job manager with the following configuration keys:
- `jobmanager.web.backpressure.refresh-interval`: Time after which available stats are deprecated and need to be refreshed (DEFAULT: 60000, 1 min).
- `jobmanager.web.backpressure.num-samples`: Number of stack trace samples to take to determine back pressure (DEFAULT: 100).
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/internals/general_arch.md
----------------------------------------------------------------------
diff --git a/docs/internals/general_arch.md b/docs/internals/general_arch.md
index c599a01..612fb6b 100644
--- a/docs/internals/general_arch.md
+++ b/docs/internals/general_arch.md
@@ -83,7 +83,7 @@ You can click on the components in the figure to learn more.
## Projects and Dependencies
The Flink system code is divided into multiple sub-projects. The goal is to reduce the number of
-dependencies that a project implementing a Flink progam needs, as well as to faciltate easier testing
+dependencies that a project implementing a Flink program needs, as well as to faciltate easier testing
of smaller sub-modules.
The individual projects and their dependencies are shown in the figure below.
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/internals/job_scheduling.md
----------------------------------------------------------------------
diff --git a/docs/internals/job_scheduling.md b/docs/internals/job_scheduling.md
index cce78d9..1e1da97 100644
--- a/docs/internals/job_scheduling.md
+++ b/docs/internals/job_scheduling.md
@@ -60,8 +60,8 @@ and reacts to finished tasks or execution failures.
The JobManager receives the {% gh_link /flink-runtime/src/main/java/org/apache/flink/runtime/jobgraph/ "JobGraph" %},
which is a representation of the data flow consisting of operators ({% gh_link /flink-runtime/src/main/java/org/apache/flink/runtime/jobgraph/JobVertex.java "JobVertex" %})
and intermediate results ({% gh_link /flink-runtime/src/main/java/org/apache/flink/runtime/jobgraph/IntermediateDataSet.java "IntermediateDataSet" %}).
-Each operator has properies, like the parallelism and the code that it executes.
-In addition, the JobGraph has a set of attached libraries, that are neccessary to execute the code of the operators.
+Each operator has properties, like the parallelism and the code that it executes.
+In addition, the JobGraph has a set of attached libraries, that are necessary to execute the code of the operators.
The JobManager transforms the JobGraph into an {% gh_link /flink-runtime/src/main/java/org/apache/flink/runtime/executiongraph/ "ExecutionGraph" %}.
The ExecutionGraph is a parallel version of the JobGraph: For each JobVertex, it contains an {% gh_link /flink-runtime/src/main/java/org/apache/flink/runtime/executiongraph/ExecutionVertex.java "ExecutionVertex" %} per parallel subtask. An operator with a parallelism of 100 will have one JobVertex and 100 ExecutionVertices.
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/internals/monitoring_rest_api.md
----------------------------------------------------------------------
diff --git a/docs/internals/monitoring_rest_api.md b/docs/internals/monitoring_rest_api.md
index 97a5c51..ca1cf13 100644
--- a/docs/internals/monitoring_rest_api.md
+++ b/docs/internals/monitoring_rest_api.md
@@ -39,7 +39,7 @@ The monitoring API is backed by a web server that runs as part of the *JobManage
In the case of multiple JobManagers (for high availability), each JobManager will run its own instance of the monitoring API, which offers information about completed and running job while that JobManager was elected the cluster leader.
-**NOTE:** Currently, the monitoring API is started together with the new web dashboard. To enable it, one need to add the following entry to the `flink-conf.yaml` in order to activate the new dashboard instread of the old dashboard: `jobmanager.new-web-frontend: true`
+**NOTE:** Currently, the monitoring API is started together with the new web dashboard. To enable it, one need to add the following entry to the `flink-conf.yaml` in order to activate the new dashboard instead of the old dashboard: `jobmanager.new-web-frontend: true`
## Developing
@@ -169,11 +169,11 @@ Sample Result:
**`/joboverview/running`**
-Jobs, groupes by status, each with a small summary of its status. The same as `/joboverview`, but containing only currently running jobs.
+Jobs, grouped by status, each with a small summary of its status. The same as `/joboverview`, but containing only currently running jobs.
**`/joboverview/completed`**
-Jobs, groupes by status, each with a small summary of its status. The same as `/joboverview`, but containing only completed (finished, canceled, or failed) jobs.
+Jobs, grouped by status, each with a small summary of its status. The same as `/joboverview`, but containing only completed (finished, canceled, or failed) jobs.
### Details of a Running or Completed Job
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/setup/config.md
----------------------------------------------------------------------
diff --git a/docs/setup/config.md b/docs/setup/config.md
index db189a0..14e9d21 100644
--- a/docs/setup/config.md
+++ b/docs/setup/config.md
@@ -83,7 +83,7 @@ The default fraction for managed memory can be adjusted using the `taskmanager.m
### Memory and Performance Debugging
-These options are useful for debugging a Flink application for memory and garbage collection related isues, such as performance and out-of-memory process kills or exceptions.
+These options are useful for debugging a Flink application for memory and garbage collection related issues, such as performance and out-of-memory process kills or exceptions.
- `taskmanager.debug.memory.startLogThread`: Causes the TaskManagers to periodically log memory and Garbage collection statistics. The statistics include current heap-, off-heap, and other memory pool utilization, as well as the time spent on garbage collection, by heap memory pool.
@@ -97,7 +97,7 @@ Flink supports Kerberos authentication of Hadoop services such as HDFS, YARN, or
other versions have critical bugs which might fail the Flink job
unexpectedly.**
-While Hadoop uses Kerberos tickets to authenticate users with services initially, the authentication process continues differently afterwards. Instead of saving the ticket to authenticate on a later access, Hadoop creates its own security tockens (DelegationToken) that it passes around. These are authenticated to Kerberos periodically but are independent of the token renewal time. The tokens have a maximum life span identical to the Kerberos ticket maximum life span.
+While Hadoop uses Kerberos tickets to authenticate users with services initially, the authentication process continues differently afterwards. Instead of saving the ticket to authenticate on a later access, Hadoop creates its own security tokens (DelegationToken) that it passes around. These are authenticated to Kerberos periodically but are independent of the token renewal time. The tokens have a maximum life span identical to the Kerberos ticket maximum life span.
Please make sure to set the maximum ticket life span high long running jobs. The renewal time of the ticket, on the other hand, is not important because Hadoop abstracts this away using its own security tocken renewal system. Hadoop makes sure that tickets are renewed in time and you can be sure to be authenticated until the end of the ticket life time.
@@ -220,7 +220,7 @@ definition. This scheme is used **ONLY** if no other scheme is specified (explic
### Runtime Algorithms
-- `taskmanager.runtime.hashjoin-bloom-filters`: Flag to activate/deactivate bloomfilters in the hybrid hash join implementation. In cases where the hash join needs to spill to disk (datasets larger than the reserved fraction of memory), these bloom filters can greatly reduce the number of spilled records, at the cost some CPU cycles. (DEFAULT: false)
+- `taskmanager.runtime.hashjoin-bloom-filters`: Flag to activate/deactivate bloom filters in the hybrid hash join implementation. In cases where the hash join needs to spill to disk (datasets larger than the reserved fraction of memory), these bloom filters can greatly reduce the number of spilled records, at the cost some CPU cycles. (DEFAULT: false)
- `taskmanager.runtime.max-fan`: The maximal fan-in for external merge joins and fan-out for spilling hash tables. Limits the number of file handles per operator, but may cause intermediate merging/partitioning, if set too small (DEFAULT: 128).
- `taskmanager.runtime.sort-spilling-threshold`: A sort operation starts spilling when this fraction of its memory budget is full (DEFAULT: 0.8).
@@ -237,7 +237,7 @@ definition. This scheme is used **ONLY** if no other scheme is specified (explic
- `yarn.heartbeat-delay` (Default: 5 seconds). Time between heartbeats with the ResourceManager.
-- `yarn.properties-file.location` (Default: temp directory). When a Flink job is submitted to YARN, the JobManager's host and the number of available processing slots is written into a properties file, so that the Flink clientis able to pick those details up. This configuration parameter allows changing the default location of that file (for example for environments sharing a Flink installation between users)
+- `yarn.properties-file.location` (Default: temp directory). When a Flink job is submitted to YARN, the JobManager's host and the number of available processing slots is written into a properties file, so that the Flink client is able to pick those details up. This configuration parameter allows changing the default location of that file (for example for environments sharing a Flink installation between users)
- `yarn.application-master.env.`*ENV_VAR1=value* Configuration values prefixed with `yarn.application-master.env.` will be passed as environment variables to the ApplicationMaster/JobManager process. For example for passing `LD_LIBRARY_PATH` as an env variable to the ApplicationMaster, set:
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/setup/local_setup.md
----------------------------------------------------------------------
diff --git a/docs/setup/local_setup.md b/docs/setup/local_setup.md
index 54864f0..43a9b79 100644
--- a/docs/setup/local_setup.md
+++ b/docs/setup/local_setup.md
@@ -102,7 +102,7 @@ Note: The ``bin`` folder of your Java Runtime Environment must be included in Wi
$ cd flink
$ cd bin
$ start-local.bat
-Starting Flink job manager. Webinterface by default on http://localhost:8081/.
+Starting Flink job manager. Web interface by default on http://localhost:8081/.
Do not close this batch window. Stop job manager by pressing Ctrl+C.
~~~
http://git-wip-us.apache.org/repos/asf/flink/blob/4fbd8eb9/docs/setup/yarn_setup.md
----------------------------------------------------------------------
diff --git a/docs/setup/yarn_setup.md b/docs/setup/yarn_setup.md
index f24f451..aa0f7a4 100644
--- a/docs/setup/yarn_setup.md
+++ b/docs/setup/yarn_setup.md
@@ -221,7 +221,7 @@ Note: It is possible to combine `-m yarn-cluster` with a detached YARN submissio
Flink's YARN client has the following configuration parameters to control how to behave in case of container failures. These parameters can be set either from the `conf/flink-conf.yaml` or when starting the YARN session, using `-D` parameters.
- `yarn.reallocate-failed`: This parameter controls whether Flink should reallocate failed TaskManager containers. Default: true
-- `yarn.maximum-failed-containers`: The maximum number of failed containers the ApplicationMaster accepts until it fails the YARN session. Default: The number of initally requested TaskManagers (`-n`).
+- `yarn.maximum-failed-containers`: The maximum number of failed containers the ApplicationMaster accepts until it fails the YARN session. Default: The number of initially requested TaskManagers (`-n`).
- `yarn.application-attempts`: The number of ApplicationMaster (+ its TaskManager containers) attempts. If this value is set to 1 (default), the entire YARN session will fail when the Application master fails. Higher values specify the number of restarts of the ApplicationMaster by YARN.
## Debugging a failed YARN session
@@ -240,11 +240,11 @@ yarn logs -applicationId <application ID>
Note that it takes a few seconds after the session has finished until the logs show up.
-### YARN Client console & Webinterfaces
+### YARN Client console & Web interfaces
The Flink YARN client also prints error messages in the terminal if errors occur during runtime (for example if a TaskManager stops working after some time).
-In addition to that, there is the YARN Resource Manager webinterface (by default on port 8088). The port of the Resource Manager web interface is determined by the `yarn.resourcemanager.webapp.address` configuration value.
+In addition to that, there is the YARN Resource Manager web interface (by default on port 8088). The port of the Resource Manager web interface is determined by the `yarn.resourcemanager.webapp.address` configuration value.
It allows to access log files for running YARN applications and shows diagnostics for failed apps.
@@ -261,7 +261,7 @@ ranges configured, users can also submit jobs to Flink crossing the firewall.
Currently, two services are needed to submit a job:
- * The JobManager (ApplicatonMaster in YARN)
+ * The JobManager (ApplicationMaster in YARN)
* The BlobServer running within the JobManager.
When submitting a job to Flink, the BlobServer will distribute the jars with the user code to all worker nodes (TaskManagers).