Spark, Hive and Presto are all very different code bases. Spark is made up of 500K lines of Scala, 110K lines of Java and 40K lines of Python. Presto is made up of 600K lines of Java. Hive is made up of over one million lines of Java and 100K lines of C++ code. Any libraries they share are out-weighted by the unique approaches they’ve taken in the architecture surrounding their SQL parsers, query planners, optimizers, code generators and execution engines when it comes to tabular form conversion.
I recently benchmarked Spark 2.4.0 and Presto 0.214 and found that Spark out-performed Presto when it comes to ORC-based queries. In this post I’m going to examine the ORC writing performance of these two engines plus Hive and see which can convert CSV files into ORC files the fastest.
The results surprised me.
Sometimes you need transform you RRDs to DataFrames because DataFrames have a lot optimization options.
Let’s see how this is done.
This works, but there is a performance cost to converting a large RDD to a DataFrame (or vice versa). With that in mind, sticking to one type when you can is typically better.
If you are building a big data solution in the cloud, you will likely be landing most of the source data into a data lake. And much of this data will need to be transformed (i.e. cleaned and joined together – the “T” in ETL). Since the data lake is just storage (i.e. Azure Data Lake Storage Gen2 or Azure Blob Storage), you need to pick a product that will be the compute and will do the transformation of the data. There is good news and bad news when it comes to which product to use. The good news is there are a lot of products to choose from. The bad news is there are a lot of products to choose from :-). I’ll try to help your decision-making by talking briefly about most of the Azure choices and the best use cases for each when it comes to transforming data (although some of these products also do the Extract and Load part
The only surprise is the non-mention of Azure Data Lake Analytics, and there is a good conversation in the comments section explaining why.
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.