RestAPIEnrich

ProphecySparkBasicsPython 0.0.1+UC Dedicated Cluster 14.3+UC Standard Cluster 15.4+Livy Not Supported

Enriches the DataFrame by adding column(s) with content from REST API output based on the given configuration.

Parameters

Each property can either be set as a static value or a value from an existing column of the input DataFrame. Please refer to the examples in the description column of each parameter for reference on how the string value should be formed.

Parameter	Description	Required	Default
method	method for the new Request object: GET, OPTIONS, HEAD, POST, PUT, PATCH, or DELETE.	true
url	URL for the REST API.	true
params	Dictionary, list of tuples or bytes to send in the query string for the Request. eg: {"key1":"value1", "key2": value2, "key3": ["value1", "value2"]}	false
data	Dictionary to send in the body of the Request. eg: {"key1":"value1", "key2": value2}	false
JSON	A JSON serializable Python object to send in the body of the Request. eg: {"key1":"value1", "key2": value2}	false
headers	Dictionary of HTTP Headers to send with the Request. eg: {"key1":"value1", "key2": "value2"}	false
cookies	Dictionary to send with the Request. eg: {"key1":"value1", "key2": "value2"}	false
auth	Auth tuple to enable Basic/Digest/Custom HTTP Auth. eg: user:pass	false
timeout	How 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: 0.5:0.25	false
allow redirects	Enable/disable GET/OPTIONS/POST/PUT/PATCH/DELETE/HEAD redirection. eg: true or false	false	true
proxies	Dictionary mapping protocol to the URL of the proxy. eg: {"https" : "https://1.1.0.1:80"}	false
verify	Either 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-file	false	true
stream	if False, the response content will be immediately downloaded. eg: true or false	false
cert	if String, path to SSL client cert file (.pem). eg. dbfs:/path-to-file. If Tuple, (‘cert’, ‘key’) pair. eg: cert:key.	false
parse content	Parse content as JSON (to make the schema available, enable custom schema, and click infer from cluster at the bottom left in the output tab)	false	false

info

1. To store sensitive information like API key (headers), auth etc., Databricks secrets can be used as shown in Example below.
2. 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.
3. 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.

Please make sure that cluster is connected while using the parse content option to infer the schema from cluster for the first time.

note

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.

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.

Example 2

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

Generated Code

Python

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]))
    )