We can see that there are no libraries installed and scoped specifically to this notebook. Now I’m going to install a later version of SciPy, restart the python interpreter, and then run that same helper function we ran previously to list any libraries installed and scoped specifically to this notebook session. When using the list() function PyPI libraries scoped to this notebook session are displayed as <library_name>-<version_number>-<repo>, and (empty) indicates that the corresponding part has no specification. This also works with wheel and egg install artifacts, but for the sake of this example we’ll just be installing the single package directly.
This does seem easier than dropping to a shell and installing with Pip, especially if you need different versions of libraries.
Databricks is a recent addition to Azure that is greatly influencing the technology choices that people are making when determining how to process data. Prior to the introduction of Databricks to Azure in March of 2018, if you had a lot of unstructured data which was stored in HDFS clusters, and wanted to analyze it in a scalable fashion, the choice was Data Lake and using USQL with Data Lake Analytics. With the introduction of Databricks, there is now a choice for analysis between Data Lake Analytics and Databricks for analyzing data.
Click through for the comparison.
When scoring Python models as Apache Spark UDFs, users can now filter UDF outputs by selecting from an expanded set of result types. For example, specifying a result type of
pyspark.sql.types.DoubleTypefilters the UDF output and returns the first column that contains double precision scalar values. Specifying a result type of
pyspark.sql.types.ArrayType(DoubleType)returns all columns that contain double precision scalar values. The example code below demonstrates result type selection using the
result_typeparameter. And the short example notebook illustrates Spark Model logged and then loaded as a Spark UDF.
Read on for a pretty long list of updates.
Sqoop performed so much better almost instantly, all you needed to do is to set the number of mappers according to the size of the data and it was working perfectly.
Since both Spark and Sqoop are based on the Hadoop map-reduce framework, it’s clear that Spark can work at least as good as Sqoop, I only needed to find out how to do it. I decided to look closer at what Sqoop does to see if I can imitate that with Spark.
By turning on the verbose flag of Sqoop, you can get a lot more details. What I found was that Sqoop is splitting the input to the different mappers which makes sense, this is map-reduce after all, Spark does the same thing. But before doing that, Sqoop does something smart that Spark doesn’t do.
Read on to see what in particular Sqoop does, and how you can use that in your Spark code.
The next major enhancement was the addition of a lot of new built-in functions, including higher-order functions, to deal with complex data types easier.
Spark 2.4 introduced 24 new built-in functions, such as
array_max/min, etc., and 5 higher-order functions, such as
The entire list can be found here.
Earlier, for manipulating the complex types (e.g. array type) directly, there are two typical solutions:
1) exploding the nested structure into individual rows, and applying some functions, and then creating the structure again.
2) building a User Defined Function (UDF).
In contrast, the new built-in functions can directly manipulate complex types, and the higher-order functions can manipulate complex values with an anonymous lambda function similar to UDFs but with much better performance.
2.4 was a big release, so check this out for a great summary of the improvements it brings.
An image data source addresses many of these problems by providing the standard representation you can code against and abstracts from the details of a particular image representation.
Apache Spark 2.3 provided the ImageSchema.readImages API (see Microsoft’s post Image Data Support in Apache Spark), which was originally developed in the MMLSpark library. In Apache Spark 2.4, it’s much easier to use because it is now a built-in data source. Using the image data source, you can load images from directories and get a DataFrame with a single image column.
This blog post describes what an image data source is and demonstrates its use in Deep Learning Pipelines on the Databricks Unified Analytics Platform.
If you’re interested in working with convolutional neural networks or otherwise need to analyze image data, check it out.
The major problem of the Lambda architecture is that we have to build two separate pipelines, which can be very complex, and, ultimately, difficult to combine the processing of batch and real-time data, however, it is now possible to overcome such limitation if we have the possibility to change our approach.
Databricks Delta delivers a powerful transactional storage layer by harnessing the power of Apache Spark and Databricks File System (DBFS). It is a single data management tool that combines the scale of a data lake, the reliability and performance of a data warehouse, and the low latency of streaming in a single system. The core abstraction of Databricks Delta is an optimized Spark table that stores data as parquet files in DBFS and maintains a transaction log that tracks changes to the table.
It’s an interesting contrast and I recommend reading the whole thing.
1. Creating Key/Value Pair RDD:
The pair RDD arranges the data of a row into two parts. The first part is the Key and the second part is the Value. In the below example, I used a
parallelizemethod to create a RDD, and then I used the
lengthmethod to create a Pair RDD. The key is the length of the each word and the value is the word itself.
scala> val rdd = sc.parallelize(List("hello","world","good","morning"))rdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD at parallelize at <console>:24scala> val pairRdd = rdd.map(a => (a.length,a))pairRdd: org.apache.spark.rdd.RDD[(Int, String)] = MapPartitionsRDD at map at <console>:26scala> pairRdd.collect().foreach(println)(5,hello)(5,world)(4,good)
Click through for more operations. Spark is a bit less KV-centric than classic MapReduce jobs, but there are still plenty of places where you want to use them.
Recently, my company faced the serious challenge of loading a 10 million rows of CSV-formatted geographic data to MongoDB in real-time.
We first tried to make a simple Python script to load CSV files in memory and send data to MongoDB. Processing 10 million rows this way took 26 minutes!
26 minutes for processing a dataset in real-time is unacceptable so we decided to proceed differently.
I’m not sure the test was totally fair, but the results comport to my biases… There is some good advice here: storing data in optimized formats (Parquet in this instance) can make a big difference, Spark is useful for ETL style operations, and Scala is generally the fastest language in the Spark world.
The latest release of Apache Hive 3 (part of HDP 3) provides significant new capabilities including ACID support for data ingest. This functionality has many applications, a crucial one being privacy support for data modifications and deletions for GDPR. In addition, ACID also significantly reduces the time to ingest for data, thereby improving data freshness for Hive queries. To provide these features, Hive needs to take full control of the files that store the table data and thus this data is no longer directly accessible by third party systems like Apache Spark. Thus Apache Spark’s built-in support for Hive table data is no longer supported for data managed by Hive 3.
At the same time, Apache Spark has become the de-facto standard for a wide variety complex processing use cases on Big Data. This includes data stored in Hive 3 tables and thus we need a way to provide efficient, high-performance, ACID compliant access to Hive 3 table data from Spark. Fortunately, Apache Spark supports a pluggable approach for various data sources and Apache Hive itself can also be considered as one data source. We have implemented the Hive Warehouse Connector (HWC) as library to provide first class support for Spark to read Hive 3 data for subsequent complex processing (like machine learning) in Spark.
Spark is also commonly used to ETL raw data into Hive tables and this scenario should continue to be supported in the Hive ACID world. To do that, HWC integrates with the latest Hive Streaming APIs to support ingest into Hive both from batch jobs as well as structured streaming jobs.
Overall the Hive Warehouse connector provide efficient read write access to Hive warehouse data from Spark jobs, while providing transparent user identity propagation and maintaining consistent security and access control.
Spark has had some dependencies on Hive (or at least expectations of certain Hive conventions like /tmp/hive existing), but the two systems have historically been more (friendly) competitors than tools integrated in the same chain.