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Dependencies:
  • ProphecySparkBasicsPython 0.0.1+
Cluster requirements:
  • UC dedicated clusters 14.3+ supported
  • UC standard clusters 15.4+ supported
  • Livy clusters not supported
Enriches the DataFrame by adding column(s) with content from REST API output based on the given configuration.

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Parameters

Each property can either be set as a static value or a value from an existing column of the input DataFrame. All input parameters are expected to be in string format. Other column types such as array/JSON/struct can be created using combination of aggregate/window gems along with reformat component and then can be cast as string prior to passing the column in RestAPIEnrich gem as needed. The following table lists all parameters of the RestAPIEnrich gem.
ParameterDescriptionRequiredDefault
methodmethod for the new Request object: GET, OPTIONS, HEAD, POST, PUT, PATCH, or DELETE.true
urlURL for the REST API.true
paramsDictionary, list of tuples or bytes to send in the query string for the Request. eg: {"key1":"value1", "key2": value2, "key3": ["value1", "value2"]}false
dataDictionary to send in the body of the Request. eg: {"key1":"value1", "key2": value2}false
JSONA JSON serializable Python object to send in the body of the Request. eg: {"key1":"value1", "key2": value2}false
headersDictionary of HTTP Headers to send with the Request. eg: {"key1":"value1", "key2": "value2"}false
cookiesDictionary to send with the Request. eg: {"key1":"value1", "key2": "value2"}false
authAuth tuple to enable Basic/Digest/Custom HTTP Auth. eg: user:passfalse
timeoutHow many seconds to wait for the server to send data before giving up, as a float, eg: 0.5 or a (connect timeout, read timeout) tuple. eg: 0.5:0.25false
allow redirectsEnable/disable GET/OPTIONS/POST/PUT/PATCH/DELETE/HEAD redirection. eg: true or falsefalsetrue
proxiesDictionary mapping protocol to the URL of the proxy. eg: {"https" : "https://1.1.0.1:80"}false
verifyEither a boolean, in which case it controls whether we verify the server’s TLS certificate eg: true or false or a string, in which case it must be a path to a CA bundle to use. Defaults to True. eg: dbfs:/path-to-filefalsetrue
streamif False, the response content will be immediately downloaded. eg: true or falsefalse
certif String, path to SSL client cert file (.pem). eg. dbfs:/path-to-file. If Tuple, (‘cert’, ‘key’) pair. eg: cert:key.false
parse contentParse content as JSON (to make the schema available, enable custom schema, and click infer from cluster at the bottom left in the output tab)falsefalse
Keep the following in mind as you configure the RestAPIEnrich gem:
  • To hide sensitive information like API keys and passwords, Databricks secrets can be used as shown below.
  • If the expected number of rows are very large, it’s better to provide await time in the advanced tab so you don’t overwhelm the source server or exceed any request limits.
  • For APIs which takes list of parameters as inputs, window functions like collect_list can be used before RestApiEnrich gem to reduce the number of API calls.

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Reference pipeline configurations

To reference variables from your pipeline configs:
  • Use the following syntax: ${config_name}
  • You can also use partial strings, for example: foo_${config_name}bar

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Example 1

Let’s try to fetch prices for few cryptocurrencies from Coin-API. We would be taking cryptocurrency and currency as input from DataFrame and pass url, headers as static values. Note that URL in this example is created using static base url and adding cryptocurrency and currency as inputs from DataFrame. Also, we would be using Databricks-secrets to pass headers as it requires API-key.

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Example 2

Let’s take a more complex example, where all method, url, headers, params etc are passed as values from DataFrame columns.

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Generated Code

def get_data_from_api(spark: SparkSession, in0: DataFrame) -> DataFrame:
 requestDF = in0.withColumn(
 "api_output",
 get_rest_api(
 to_json(struct(lit("GET").alias("method"), col("url"), lit(Config.coin_api_key).alias("headers"))),
 lit("")
 )
 )

 return requestDF.withColumn(
 "content_parsed",
 from_json(col("api_output.content"), schema_of_json(requestDF.select("api_output.content").take(1)[0][0]))
 )