fix(doc): make curl commands compatible with windows

Author: noorul

README.md

@@ -177,7 +177,7 @@ Let's upload the jar:
 The above jar is uploaded as app `test`.  Next, let's start an ad-hoc word count job, meaning that the job
 server will create its own SparkContext, and return a job ID for subsequent querying:
 
-    curl -d "input.string = a b c a b see" 'localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCountExample'
+    curl -d "input.string = a b c a b see" "localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCountExample"
     {
       "duration": "Job not done yet",
       "classPath": "spark.jobserver.WordCountExample",
@@ -190,7 +190,7 @@ server will create its own SparkContext, and return a job ID for subsequent quer
 NOTE: If you want to feed in a text file config and POST using curl, you want the `--data-binary` option, otherwise
 curl will munge your line separator chars.  Like:
 
-    curl --data-binary @my-job-config.json 'localhost:8090/jobs?appNam=...'
+    curl --data-binary @my-job-config.json "localhost:8090/jobs?appNam=..."
 
 NOTE2: If you want to send in UTF-8 chars, make sure you pass in a proper header to CURL for the encoding, otherwise it may assume an encoding which is not what you expect.
 
@@ -220,7 +220,7 @@ You can also append `&timeout=XX` to extend the request timeout for `sync=true`
 #### Persistent Context Mode - Faster & Required for Related Jobs
 Another way of running this job is in a pre-created context.  Start a new context:
 
-    curl -d "" 'localhost:8090/contexts/test-context?num-cpu-cores=4&memory-per-node=512m'
+    curl -d "" "localhost:8090/contexts/test-context?num-cpu-cores=4&memory-per-node=512m"
     OK⏎
 
 You can verify that the context has been created:
@@ -230,7 +230,7 @@ You can verify that the context has been created:
 
 Now let's run the job in the context and get the results back right away:
 
-    curl -d "input.string = a b c a b see" 'localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCountExample&context=test-context&sync=true'
+    curl -d "input.string = a b c a b see" "localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCountExample&context=test-context&sync=true"
     {
       "result": {
         "a": 2,
@@ -309,7 +309,7 @@ It is much more type safe, separates context configuration, job ID, named object
 
 Let's try running our sample job with an invalid configuration:
 
-    curl -i -d "bad.input=abc" 'localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCountExample'
+    curl -i -d "bad.input=abc" "localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCountExample"
 
     HTTP/1.1 400 Bad Request
     Server: spray-can/1.2.0
@@ -343,11 +343,11 @@ You have a couple options to package and upload dependency jars.
     - Use the `dependent-jar-uris` context configuration param. Then the jar gets loaded for every job.
     - The `dependent-jar-uris` can also be used in job configuration param when submitting a job. On an ad-hoc context this has the same effect as `dependent-jar-uris` context configuration param. On a persistent context the jars will be loaded for the current job and then for every job that will be executed on the persistent context.
         ````
-        curl -d "" 'localhost:8090/contexts/test-context?num-cpu-cores=4&memory-per-node=512m'
+        curl -d "" "localhost:8090/contexts/test-context?num-cpu-cores=4&memory-per-node=512m"
         OK⏎
         ````
         ````
-        curl 'localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCountExample&context=test-context&sync=true' -d '{
+        curl "localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCountExample&context=test-context&sync=true" -d '{
             dependent-jar-uris = ["file:///myjars/deps01.jar", "file:///myjars/deps02.jar"],
             input.string = "a b c a b see"
         }'

doc/EMR.md

@@ -191,7 +191,7 @@ InstanceCount=10,BidPrice=2.99,Name=sparkSlave,InstanceGroupType=CORE,InstanceTy
 3. Create test context
  ```
  # create test context
- curl -d "" 'localhost:8090/contexts/test?num-cpu-cores=1&memory-per-node=512m&spark.executor.instances=1'
+ curl -d "" "localhost:8090/contexts/test?num-cpu-cores=1&memory-per-node=512m&spark.executor.instances=1"
  # check current contexts. should return test
  curl localhost:8090/contexts
  ```
@@ -200,7 +200,7 @@ InstanceCount=10,BidPrice=2.99,Name=sparkSlave,InstanceGroupType=CORE,InstanceTy
  ```
  # run WordCount example (should be done in 1-2 sec)
  curl -d "input.string = a b c a b see" \
- 'localhost:8090/jobs?appName=testapp&classPath=spark.jobserver.WordCountExample&context=test&sync=true'
+ "localhost:8090/jobs?appName=testapp&classPath=spark.jobserver.WordCountExample&context=test&sync=true"
  ```
 
 5. Check jobs

doc/python.md

@@ -150,7 +150,7 @@ Then, running `python setup.py bdist_egg` will create a file `dist/my_job_packag
 
 If Spark Job Server is running with Python support, A Python context can be started with, for example:
 
-    curl -X POST 'localhost:8090/contexts/py-context?context-factory=spark.jobserver.python.PythonSparkContextFactory'
+    curl -X POST "localhost:8090/contexts/py-context?context-factory=spark.jobserver.python.PythonSparkContextFactory"
 
 Whereas Java and Scala jobs are packaged as Jar files, Python jobs need to be packaged as `Egg` files. A set of example jobs
 can be build using the `job-server-python/` sbt task `job-server-python/buildPyExamples`. this builds an examples Egg
@@ -162,16 +162,16 @@ in `job-server-python/target/python` so we could push this to the server as a jo
 Then, running a Python job is similar to running other job types:
 
     curl -d 'input.strings = ["a", "b", "a", "b" ]' \
-    'localhost:8090/jobs?appName=my_py_job&classPath=my_job_package.WordCountSparkJob&context=py-context'
+    "localhost:8090/jobs?appName=my_py_job&classPath=my_job_package.WordCountSparkJob&context=py-context"
 
-    curl 'localhost:8090/jobs/<job-id>'
+    curl "localhost:8090/jobs/<job-id>"
 
 ## SQLContext and HiveContext support
 
 Python support is also available for `SQLContext` and `HiveContext`. Simply launch a context using
 `spark.jobserver.python.PythonSQLContextFactory` or `spark.jobserver.python.PythonHiveContextFactory`. For example:
 
-    curl -X POST 'localhost:8090/contexts/pysql-context?context-factory=spark.jobserver.python.PythonSQLContextFactory'
+    curl -X POST "localhost:8090/contexts/pysql-context?context-factory=spark.jobserver.python.PythonSQLContextFactory"
 
 When implementing the Python job, you can simply assume that the `context` argument to `validate` and `run_job`
 is of the appropriate type. Due to dynamic typing in Python, this is not enforced in the method definitions. For example:
@@ -224,9 +224,9 @@ The input to the job can be provided as a conf file, e.g. with the contents:
 Then we can submit the `SQLContext` based job:
 
     curl -d @sqlinput.conf \
-    'localhost:8090/jobs?appName=example_jobs&classPath=example_jobs.sql_average.SQLAverageJob&context=pysql-context'
+    "localhost:8090/jobs?appName=example_jobs&classPath=example_jobs.sql_average.SQLAverageJob&context=pysql-context"
 
-    curl 'localhost:8090/jobs/<job-id>'
+    curl "localhost:8090/jobs/<job-id>"
 
 When complete, we get output such as: