Here we learned to read a table of data from a MongoDB database in Pyspark. Note: if you cannot read the data from the table, you need to check your privileges on the MongoDB database to the user. Query1 = spark.sql("SELECT * FROM books_tbl ") We will print the top 5 rows from the dataframe as shown below. Here we are going to read the content of the table by querying. Step 5: To view or query the content of the table Here we are going to create a temporary table from the dataframe as below.īooks_table = books_tbl.createOrReplaceTempView("books_tbl") Here we will read the schema of the stored table as a dataframe, as shown below. Note: if your database has authentication, then only we will provide the credentials otherwise will pass the URL as "mongodb://127.0.0.1:27017/dezyre.books" In the URL, hduser is username, and big data is the password of the authentication credentials of the MongoDB database. To read the data frame, we will use the read() method through the URL. Here we are going to read the data table from the MongoDB database and create the DataFrames. Note: we need to specify the mongo spark connector which is suitable for your spark version. config('', ':mongo-spark-connector_2.12:3.0.1') \Īs shown in the above code, If you specified the and configuration options when you started pyspark, the default SparkSession object uses them. In this scenario, we are going to import the pyspark and pyspark SQL modules and create a spark session as below : Here we have a table or collection of books in the dezyre database, as shown below. Here in this scenario, we will read the data from the MongoDB database table as shown below.
The below codes can be run in Jupyter notebook or any python console.Install pyspark or spark in Ubuntu click here.Install Ubuntu in the virtual machine click here.In this scenario, we are going to read a table of data from a MongoDB database. Data merging and data aggregation are an essential part of the day-to-day activities in big data platforms. For example, loading the data from JSON, CSV. In most big data scenarios, DataFrame in Apache Spark can be created in multiple ways: It can be made using different data formats. "start_of_day" : ISODate("T00:00:00.000Z")Ĭan't say if it any faster than user1083621's method.Recipe Objective: How to read a table of data from a MongoDB database in Pyspark? exclude the time of the day) I guess, some smart projection should be applied.
I have some docs in mongo that looks something like this: , How can I aggregate/count/group the errors by DAY only (i.e.
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