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Injecting Databricks DataFrame into a Power BI Push Dataset

Using Python and the Power BI REST APINow, why would you want to do that?There are some scenarios where this approach may be beneficial. To get the full picture and establish the landscape let’s first answer two questions:Why a Databricks DataFrame?Recently Databricks became an integral part of the Modern Datawarehouse approach when aiming for the Azure cloud. Its wide usage in data transformation begs for a richer variety of data destinations. The usual and most widely used persistence is the file store (lake, blob, etc.). It’s fine with large volumes of data, but then we have to go a long way before reaching the data presentation (additional storage layers, SQL, Tabular data model etc…).What if we want to instantly update a Power BI report directly from Databricks? It could be a small dataset feeding a dashboard for example. It could be business data or data-related metrics that we want to see in (near)real-time. Or we want to monitor the data transformation process continuously in a Databricks streaming scenario.Why a Push Dataset?We can do real-time streaming in Power BI by using Streaming or PubNub streaming datasets, which is fine, but let’s see some of the advantages of using a Push dataset:You can build a report on top of a Push datasetYou can pin report visuals to a dashboard, and they will update in real-time on each data pushData is stored permanently in Power BIYou don’t need a data gateway in placeFor a more detailed comparison of all the real-time streaming methods, please check here.If you think that all this makes sense, then continue further.ImplementationSince we’ll be utilising the Power BI REST API there are some limitations that we need to be aware of upfront. You can see them here.In order to be able to call the Power BI API you need to register an application and set proper permissions. Follow the steps here. App registration details below:After creating the app registration, you should grant permissions in the Azure portal:Without granting these permissions from the Azure portal you won’t be able to get authorisation token and use the REST API.Now that we’re all set-up let’s go straight to the point.My initial intention was to show you how to build the whole thing step-by-step in this post. But then it become complicated and I decided that an abstraction is needed here to keep things simple. That’s why I wrapped up all the “boring” stuff in a Python class called pbiDatasetAPI, which you can find on GitHub here. The comments in the code should be enough to understand the logic. The methods of this class will take care of:AuthenticationHTTP requestsHTTP requests JSON body generation (data and metadata)Data type conversion (Spark to EDM)Wrapping in a Python function of all the Power BI REST API operations that we need to performIn order to start using it you should import a notebook (using the above-mentioned URL) in your Databricks Workspace:Now that the class notebook is imported you can create a new Python notebook in the same folder to test how it’s working. Let’s call it “Inject DataFrame into Power BI Push Dataset”. First, we’ll execute our class notebook:%run "./pbiDatasetAPI" Next, we’ll need a DataFrame with data that will be pushed to the Power BI Push dataset. We can use some of the sample datasets which come with Databricks (in this case Amazon reviews):dfInject = spark.read.parquet('dbfs:/databricks-datasets/amazon/test4K') dfInject = dfInject.select("brand", "img", "price", "rating", "review", "time").limit(200) We take 200 rows, which is just enough.In the next command we’ll create some variables and instantiate the pbiDatasetAPI class:# Initialise variables username = "<USER_EMAIL>" password = "<USER_PASSWORD>" application_id = "********-****-****-****-************" # Power BI application ID groupId = None # Set to None if not using Power BI groups datasetName = "InjectedDataset" # The name of the Power BI dataset tableNames = ["AmazonReviews"] # Table name or list of table names dataFrames = [dfInject] # DataFrame name or list of DataFrame names # Create a pbiDatasetAPI class instance pbi = pbiDatasetAPI(username, password, application_id) You should set your username and password, and the application ID obtained in the previous steps. Optionally provide also a group ID or set it to None if you’re not using groups on powerbi.com.The tableNames and dataFrames variables are lists, because we may want to insert multiple DataFrames in multiple tables. In our case it’s one DataFrame to one table.Let’s create a dataset with one table in Power BI:# Create the dataset and the table in PBI pbi.executePBIOperation("postdataset", groupId = groupId, datasetName = datasetName, tableNames = tableNames, dataFrames = dataFrames, reCreateIfExists = True) The next step is to post all the DataFrame’s rows to the Push dataset.# Get the datasetKey for the dataset (by name) datasetKey = pbi.executePBIOperation("getdatasetbyname", groupId = groupId, datasetName = datasetName)[0]["id"] # Insert the contents of the DataFrame in the PBI dataset table pbi.executePBIOperation("postrows", groupId = groupId, datasetKey = datasetKey, tableNames = tableNames, dataFrames = dataFrames) This is where the magic happens. One important note here is that the dataset key is always unique. This does not apply to the dataset’s name, which means that we can have multiple datasets with the same name.You can see the newly created dataset on powerbi.com:You can create a report on top of this dataset and pin visuals to a dashboard (tiles will be updated automatically upon data change).Now, let’s try to manipulate the metadata a bit – change the data type of the “rating” column in the DataFrame from double to string and update the Push dataset accordingly:# Change the data type of the column 'rating' from double to string dfInjectModified = dfInject.withColumn("rating", dfInject.rating.cast("string")) # Get the datasetKey for the dataset (by name) datasetKey = pbi.executePBIOperation("getdatasetbyname", groupId = groupId, datasetName = datasetName)[0]["id"] # Update the metadata of the Power BI table pbi.executePBIOperation("puttable", groupId = groupId, datasetKey = datasetKey, tableNames = tableNames, dataFrames = [dfInjectModified]) The only thing remaining now is to try and delete all the rows (it’s all or nothing – we can’t delete some of the rows) from the table in the dataset and then delete the entire dataset:# Get the datasetKey for the dataset (by name) datasetKey = pbi.executePBIOperation("getdatasetbyname", groupId = groupId, datasetName = datasetName)[0]["id"] # Delete all rows from the table(s) pbi.executePBIOperation("deleterows", groupId = groupId, datasetKey = datasetKey, tableNames = tableNames) # Get the datasetKey for the dataset (by name) datasetKey = pbi.executePBIOperation("getdatasetbyname", groupId = groupId, datasetName = datasetName)[0]["id"] # Delete the dataset pbi.executePBIOperation("deletedatasetbyid", groupId = groupId, datasetKey = datasetKey) All the code above you can import in a notebook using this URL.If you closely examine the Power BI REST API documentation here, you’ll find that the pbiDatasetAPI class is not completely finished yet. There’s more that needs to be done, like:Create measures with DAXCreate table relationshipsSet cross-filtering behaviouretc.I intend to update the class in the future so that all the features will be available. Check GitHub now and then for updates.

Installing Databricks Cluster Libraries from a Python notebook

Working with interactive clusters in Databricks makes it possible to manually install libraries using the workspace UI. It’s done once and for all. You don’t have to worry about it anymore. Creating job clusters is another story. You must take care of library installations prior to executing the notebooks which reference these libraries. In the case you’re using Azure Data Factory to orchestrate the whole process you’re lucky, because appending libraries to job clusters is an out-of-the-box functionality. For all other scenarios using the Databricks REST API is one possible option. In fact, you can do this right from a Python notebook. That’s what I’m going to demonstrate in the following lines. Let’s have a look at the REST API documentation first. We’ll be using the Cluster Status and Install endpoints only. For installing a library, we need to provide the library source and its properties. We need to create a proper HTTP request body in JSON format including the library source and properties. Here’s one example: { "pypi": { "package": "simplejson", "repo": "http://my-pypi-mirror.com" } } Here "pypi" is the source and {"package": "simplejson", "repo": "http://my-pypi-mirror.com"} are its properties. For more examples check the documentation here. Let’s create a new Python notebook and then some functions in it. The code is simple and self-explanatory. Comments are available where needed. We’ll be using only json and requests libraries:import json import requests from requests.auth import HTTPBasicAuth Now, let’s create a REST API wrapper function: def adbAPI(endPoint, body, method = "GET", region, token): """Execute HTTP request against Databricks REST API 2.0""" domain = region + ".azuredatabricks.net" baseURL = "https://%s/api/" % (domain) if method.upper() == "GET": response = requests.get( baseURL + endPoint , auth = HTTPBasicAuth("token", token) , json = body ) else: response = requests.post( baseURL + endPoint , auth = HTTPBasicAuth("token", token) , json = body ) return response As parameters we’ll take the API endpoint, HTTP request body, HTTP method (GET or POST), Databricks workspace region (westeurope, northeurope, etc.) and, finally, a Databricks token.   Next is a helper function for translating the library status response into a human readable format: def describeClusterLibraryState(source, clusterId, status): """Converts cluster library status response to a verbose message""" result_map = { "NOT_INSTALLED" : "{} library is not installed on cluster {}.".format(source.title(), clusterId) , "INSTALLED" : "{} library is already installed on cluster {}.".format(source.title(), clusterId) , "PENDING" : "Pending installation of {} library on cluster {} . . .".format(source, clusterId) , "RESOLVING" : "Retrieving metadata for the installation of {} library on cluster {} . . .".format(source, clusterId) , "INSTALLING" : "Installing {} library on cluster {} . . .".format(source, clusterId) , "FAILED" : "{} library failed to install on cluster {}.".format(source.title(), clusterId) , "UNINSTALL_ON_RESTART": "{} library installed on cluster {} has been marked for removal upon cluster restart.".format(source.title(), clusterId) } return result_map[status.upper()] The parameters here include the library source, the cluster ID and the API’s status response. Now let’s get to the serious business. Obviously, we need a way to check the current state of the library to see if it’s not already installed or if something went wrong during installation: def getClusterLibraryStatus(source, properties, dbRegion, dbToken, verbose = True): """Gets the current library status """ source = source.lower() # Validate input assert source in ("jar", "egg", "whl", "pypi", "maven", "cran"), \ "Error: Invalid library source specified. Valid sources are: jar, egg, whl, pypi, maven, cran" assert properties is not None, \ "Error: Empty properties provided" # Get the cluster ID from the Spark environment clusterId = spark.conf.get("spark.databricks.clusterUsageTags.clusterId") # Set default result to not installed result = describeClusterLibraryState(source, clusterId, "NOT_INSTALLED") if verbose else "NOT_INSTALLED" # Execute REST API request to get the library statuses libStatuses = adbAPI("2.0/libraries/cluster-status?cluster_id=" + clusterId, None, "GET", dbRegion, dbToken) if libStatuses.status_code == 200: statuses = libStatuses.json() if "library_statuses" in statuses: for status in statuses["library_statuses"]: if status["library"] == {source: properties}: if verbose: result = describeClusterLibraryState(source, clusterId, status["status"]) else: result = status["status"] else: print(status) return result Parameters here include the library source and properties, Databricks region and token and a verbose flag, which if set to true (default) will make the function return human readable library status.   Finally, we have to create the most important function that will actually install the library: def installClusterLibrary(source, properties, dbRegion, dbToken): """ Installs a cluster library given correct source and properties are provided For examples see https://docs.databricks.com/api/latest/libraries.html#install """ source = source.lower() # Validate input assert source in ("jar", "egg", "whl", "pypi", "maven", "cran"), \ "Error: Invalid library source specified. Valid sources are: jar, egg, whl, pypi, maven, cran" assert properties is not None, \ "Error: Empty properties provided" # Get the cluster ID from the Spark environment clusterId = spark.conf.get("spark.databricks.clusterUsageTags.clusterId") status = getClusterLibraryStatus(source, properties, dbRegion, dbToken, False).upper() if status != "INSTALLED": # Create the HTTP request body based on the cluster ID, library source and properties body = json.loads('{"cluster_id": "' + clusterId + '", "libraries": [{"' + source + '": ' + json.dumps(properties) + '}]}') # Execute REST API request to install the library runAPI = adbAPI("2.0/libraries/install", body, "POST", dbRegion, dbToken) if runAPI.status_code == 200: print("Installation started . . .") else: print(runAPI) else: print(describeClusterLibraryState(source, clusterId, status)) return status The parameters here are: the library source and properties and the Databricks region and token. Example function calls (azure-sqldb-spark library hosted on Maven) Show the library status: print(getClusterLibraryStatus("maven", {"coordinates": "com.microsoft.azure:azure-sqldb-spark:1.0.2"}, "westeurope", "dapi********************************")) Install the library: installClusterLibrary("maven", {"coordinates": "com.microsoft.azure:azure-sqldb-spark:1.0.2"}, "westeurope", "dapi********************************") One thing to note here is that the library installation starts and runs asynchronously. This means that you can execute the getClusterLibraryStatus function multiple times after installClusterLibrary and get different results until the installation is done (or failed).

PySpark DataFrame Transformation Chaining

After working with Databricks and PySpark for a while now, its clear there needs to be as much best practice defined upfront as possible when coding notebooks. While you can get away with quite a bit when writing SQL - which is all too familiar to most of us now, the transition into other languages (from a BI background) requires a bit more thought in terms of coding standards. In this short blog, I will talk about multiple DataFrame transformations and what I believe to be the best way to go about structuring your code for them. More often than not, if you have not come from a programming background or are unfamiliar with a language, you will end up writing spaghetti code. Essentially code that reads a bit like a book, with no real structure to it, causing debugging/maintenance issues. We’ve all been there, especially at the start of a project, just wanting to get some output – but its worth trying to bear in mind some coding rules while designing to save the refactoring a few weeks down the line. Jumping into the subject matter of the blog, this came about because I noticed some of our project code when it came to transformations was made up of re-assigning to a new data frame on each transformation. While functionality it was acceptable, it didn’t feel the right way to go about things. The same can be said with re-using the same data frame (although this caused other dependency issues). It also left us with huge chunks of code which was only possible to read by using the comments to see where some transformations stopped and others started. After a small bit of research I discovered the concept of monkey patching (modifying a program to extend its local execution) the DataFrame object to include a transform function. This function is missing from PySpark but does exist as part of the Scala language already. The following code can be used to achieve this, and can be stored in a generic wrapper functions notebook to separate it out from your main code. This can then be called to import the functions whenever you need them.   # Monkey patch the DataFrame object with a transform method from pyspark.sql.dataframe import DataFrame def transform(self, f): return f(self) DataFrame.transform = transform   As part of your main code, we can then wrap the transformations into functions, passing in and returning a DataFrame. A separate statement can then be called specifying transform on the original DataFrame and the list of functions (transformations) you want to pass in. By using this method, the code is almost self-documenting as its clear what transformations you’ve then applied to move a DataFrame from one context into another. The example below only includes 2 transformations, but imagine you have 20+ to implement – this makes the code much easier to read and debug should there be an issue.   # Create started/finished timestamps and int dates in UTC as new columns in the DataFrame. def append_utc_dates(df): df = df.withColumn("finishedTimestampUTC", to_utc_timestamp(col("finished"), "EST")) \ .withColumn("startedTimestampUTC", to_utc_timestamp(col("started"), "EST")) \ .withColumn("startedDateUTC", ConvertDateTimeToIntDate(col("startedTimestampUTC")))\ .withColumn("finishedDateUTC", ConvertDateTimeToIntDate(col("finishedTimestampUTC"))) return df # Adds new attribute based on hand structure in to the DataFrame def append_BB(df): df = df.join(refHandStructure, df["structure"] == refHandStructure["structure"], "left") \ .select(df["*"], refHandStructure["handStructureName"]) \ .withColumn("BB",when(col("HSName") == "Limit", col("x")).otherwise(col("y"))) df = df.drop("handStructureName") return df # Perform hand transformations dfTransformed = dfRaw.transform(append_utc_dates) \ .transform(append_BB) display(dfTransformed)   You should then use new DataFrames for changes in grain or changes in purpose (not just for each transformation). This technique probably shouldn’t be used for some of the more basic transformations, especially if you only have 1 or 2, but more so when you have 50/100+ lines of code.  Not everything needs to be wrapped into functions, but it certainly reads better for larger notebooks!

Parsing nested JSON lists in Databricks using Python

Parsing complex JSON structures is usually not a trivial task. When your destination is a database, what you expect naturally is a flattened result set. Things get more complicated when your JSON source is a web service and the result consists of multiple nested objects including lists in lists and so on. Things get even more complicated if the JSON schema changes over time, which is often a real-life scenario. We have these wonderful Azure Logic Apps, which help us consistently get the JSON results from various sources. However, Logic Apps are not so good at parsing more complex nested structures. And they definitely don’t like even subtle source schema changes. Enter Databricks! With Databricks you get: An easy way to infer the JSON schema and avoid creating it manually Subtle changes in the JSON schema won’t break things The ability to explode nested lists into rows in a very easy way (see the Notebook below) Speed! Following is an example Databricks Notebook (Python) demonstrating the above claims. The JSON sample consists of an imaginary JSON result set, which contains a list of car models within a list of car vendors within a list of people. We want to flatten this result into a dataframe. Here you go: Blog - Nested JSON Arrays in Databricks - Databricks window.settings = {"enableUsageDeliveryConfiguration":false,"enableNotebookNotifications":true,"enableSshKeyUI":false,"defaultInteractivePricePerDBU":0.55,"enableDynamicAutoCompleteResourceLoading":false,"enableClusterMetricsUI":false,"allowWhitelistedIframeDomains":true,"enableOnDemandClusterType":true,"enableAutoCompleteAsYouType":[],"devTierName":"Community Edition","enableJobsPrefetching":true,"workspaceFeaturedLinks":[{"linkURI":"https://docs.azuredatabricks.net/index.html","displayName":"Documentation","icon":"question"},{"linkURI":"https://docs.azuredatabricks.net/release-notes/product/index.html","displayName":"Release Notes","icon":"code"},{"linkURI":"https://docs.azuredatabricks.net/spark/latest/training/index.html","displayName":"Training & 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if (window.mainJsLoadError) { var u = 'https://databricks-prod-cloudfront.cloud.databricks.com/static/c0a57b890925d4a38b701f56755414e0d7e15ba065243871740ecb804faf39d5/js/notebook-main.js'; var b = document.getElementsByTagName('body')[0]; var c = document.createElement('div'); c.innerHTML = ('Network Error' + 'Please check your network connection and try again.' + 'Could not load a required resource: ' + u + ''); c.style.margin = '30px'; c.style.padding = '20px 50px'; c.style.backgroundColor = '#f5f5f5'; c.style.borderRadius = '5px'; b.appendChild(c); } "> We've seen here how we can use Databricks to flatten JSON with just a few lines of code. Keep your eyes open for future Databricks related blogs, which will demonstrate more of the versatility of this great platform. More on some of the used functions (PySpark 2.3.0 documentation): DataFrameReader.json explode (function(root, factory) { // `root` does not resolve to the global window object in a Browserified // bundle, so a direct reference to that object is used instead. var _srcDoc = window.srcDoc; if (typeof define === "function" && define.amd) { define(['exports'], function(exports) { factory(exports, _srcDoc); root.srcDoc = exports; }); } else if (typeof exports === "object") { factory(exports, _srcDoc); } else { root.srcDoc = {}; factory(root.srcDoc, _srcDoc); } })(this, function(exports, _srcDoc) { var idx, iframes; var isCompliant = !!("srcdoc" in document.createElement("iframe")); var sandboxMsg = "Polyfill may not function in the presence of the " + "`sandbox` attribute. Consider using the `force` option."; var sandboxAllow = /\ballow-same-origin\b/; /** * Determine if the operation may be blocked by the `sandbox` attribute in * some environments, and optionally issue a warning or remove the * attribute. */ var validate = function( iframe, options ) { var sandbox = iframe.getAttribute("sandbox"); if (typeof sandbox === "string" && !sandboxAllow.test(sandbox)) { if (options && options.force) { iframe.removeAttribute("sandbox"); } else if (!options || options.force !== false) { logError(sandboxMsg); iframe.setAttribute("data-srcdoc-polyfill", sandboxMsg); } } }; var implementations = { compliant: function( iframe, content, options ) { if (content) { validate(iframe, options); iframe.setAttribute("srcdoc", content); } }, legacy: function( iframe, content, options ) { var jsUrl; if (!iframe || !iframe.getAttribute) { return; } if (!content) { content = iframe.getAttribute("srcdoc"); } else { iframe.setAttribute("srcdoc", content); } if (content) { validate(iframe, options); // The value returned by a script-targeted URL will be used as // the iFrame's content. Create such a URL which returns the // iFrame element's `srcdoc` attribute. jsUrl = "javascript: window.frameElement.getAttribute('srcdoc');"; // Explicitly set the iFrame's window.location for // compatability with IE9, which does not react to changes in // the `src` attribute when it is a `javascript:` URL, for // some reason if (iframe.contentWindow) { iframe.contentWindow.location = jsUrl; } iframe.setAttribute("src", jsUrl); } } }; var srcDoc = exports; var logError; if (window.console && window.console.error) { logError = function(msg) { window.console.error("[srcdoc-polyfill] " + msg); }; } else { logError = function() {}; } // Assume the best srcDoc.set = implementations.compliant; srcDoc.noConflict = function() { window.srcDoc = _srcDoc; return srcDoc; }; // If the browser supports srcdoc, no shimming is necessary if (isCompliant) { return; } srcDoc.set = implementations.legacy; // Automatically shim any iframes already present in the document iframes = document.getElementsByTagName("iframe"); idx = iframes.length; while (idx--) { srcDoc.set( iframes[idx] ); } });