Merge pull request #2 from MKhalusova/readme-review

Author: asm0dey

README.md

@@ -1,30 +1,30 @@
 # Kotlin Spark API
 
 
-Your next API to work with  [Spark](https://spark.apache.org/)
+Your next API to work with  [Spark](https://spark.apache.org/).
 
-We are looking to have this as a part of https://github.com/apache/spark repository. Consider this as beta-quality software.
+We are looking to have this as a part of https://github.com/apache/spark repository. Consider this beta-quality software.
 
 ## Goal
 
 This project adds a missing layer of compatibility between [Kotlin](https://kotlinlang.org/) and [Spark](https://spark.apache.org/).
 
-Despite Kotlin having first-class compatibility API, Kotlin developers might want to use familiar features like data classes and lambda expressions as simple expressions in curly braces or method references.
+Despite Kotlin having first-class compatibility API, Kotlin developers may want to use familiar features like data classes and lambda expressions as simple expressions in curly braces or method references.
 
 ## Non-goals
 
-There is no goal to replace any currently supported language or provide them with some functionality to support Kotlin language.
+There is no goal to replace any currently supported language or provide other APIs with some functionality to support Kotlin language.
 
 ## Installation
 
-Currently, there are no kotlin-spark-api artifacts in maven central, but you can obtain copy using JitPack here: [![](https://jitpack.io/v/JetBrains/kotlin-spark-api.svg)](https://jitpack.io/#JetBrains/kotlin-spark-api)
+Currently, there are no kotlin-spark-api artifacts in maven central, but you can obtain a copy using JitPack here: [![](https://jitpack.io/v/JetBrains/kotlin-spark-api.svg)](https://jitpack.io/#JetBrains/kotlin-spark-api)
 
 There is support for `Maven`, `Gradle`, `SBT`, and `leinengen` on JitPack.
 
-This project does not force you to use any concrete version of spark, but we've only tested it with spark `3.0.0-preview2`.
-We believe it should also work fine with version `2.4.5`
+This project does not force you to use any specific version of Spark, but it has only been tested it with spark `3.0.0-preview2`.
+We believe it can work with Spark `2.4.5` but we cannot guarantee that.
 
-So if you're using Maven you'll hve to add following into your `pom.xml`:
+So if you're using Maven you'll have to add the following into your `pom.xml`:
 
 ```xml
 <repositories>
@@ -55,7 +55,7 @@ First (and hopefully last) thing you need to do is to add following import to yo
 import org.jetbrains.spark.api.*
 ```
 
-Then you can create SparkSession we all remember and love
+Then you can create a SparkSession:
 
 ```kotlin
 val spark = SparkSession
@@ -65,25 +65,24 @@ val spark = SparkSession
 
 ```
 
-To create Dataset you may call `toDS` method like this
+To create a Dataset you can call `toDS` method:
 
 ```kotlin
 spark.toDS("a" to 1, "b" to 2)
 ```
 
 Indeed, this produces `Dataset<Pair<String, Int>>`. There are a couple more `toDS` methods which accept different arguments.
 
-Also, there are several interesting aliases in API, like `leftJoin`, `rightJoin` etc.
-Interesting fact about them that they're null-safe by design. For example, `leftJoin` is aware of nullability and returns `Dataset<Pair<LEFT, RIGHT?>>`.
-Note that were forcing `RIGHT` to be nullable for you as a developer to be able to handle this situation.
+Also, there are several aliases in API, like `leftJoin`, `rightJoin` etc. These are null-safe by design. For example, `leftJoin` is aware of nullability and returns `Dataset<Pair<LEFT, RIGHT?>>`.
+Note that we are forcing `RIGHT` to be nullable for you as a developer to be able to handle this situation.
 
-We know that `NullPointerException`s are hard to debug in Spark And trying hard to make them happen as rare as possible.
+We know that `NullPointerException`s are hard to debug in Spark, and we are trying hard to make them as rare as possible.
 
 ## Useful helper methods
 
 ### `withSpark`
 
-We provide you with useful function `withSpark`, which accepts everything that may be needed to run spark — properties, name, master location and so on. It also accepts a block of code to execute inside spark context.
+We provide you with useful function `withSpark`, which accepts everything that may be needed to run Spark — properties, name, master location and so on. It also accepts a block of code to execute inside Spark context.
 
 After work block ends, `spark.stop()` is called automatically.
 
@@ -99,9 +98,9 @@ withSpark {
 
 ### `withCached`
 
-It may easily happen that we need to fork our computation to several paths. To compute things only once we should call `cache`
+It can easily happen that we need to fork our computation to several paths. To compute things only once we should call `cache`
 method. But there it is hard to control when we're using cached `Dataset` and when not.
-It is also easy to forget to unpersist cached data, which may make break things unexpectably or take more memory
+It is also easy to forget to unpersist cached data, which can break things unexpectably or take more memory
 than intended.
 
 To solve these problems we introduce `withCached` function
@@ -124,10 +123,10 @@ Here we're showing cached `Dataset` for debugging purposes then filtering it. Th
 
 ## Examples
 
-You can find more examples in [examples](https://github.com/JetBrains/kotlin-spark-api/tree/master/examples/src/main/kotlin/org/jetbrains/spark/api/examples) module.
+For more, check out [examples](https://github.com/JetBrains/kotlin-spark-api/tree/master/examples/src/main/kotlin/org/jetbrains/spark/api/examples) module.
 
 ## Issues and feedback
 
 Issues and any feedback are very welcome in `Issues` here.
 
-If you find that we missed some important features — please report it, and we'll consider adding them.
+If you find that we missed some important features — let us know!