I know the advantages of Dataset (type safety etc), but i can't find any documentation related Spark Datasets Limitations.
Are there any specific scenarios where Spark Dataset is not recommended and better to use DataFrame.
Currently all our data engineering flows are using Spark (Scala)DataFrame.
We would like to make use of Dataset, for all our new flows. So knowing all the limitations/disadvantages of Dataset would help us.
EDIT: This is not similar to Spark 2.0 Dataset vs DataFrame, which explains some operations on Dataframe/Dataset. or other questions, which most of them explains the differences between rdd, dataframe and dataset and how they evolved. This is targeted to know, when NOT to use Datasets
There are a few scenarios where I find that a Dataframe (or Dataset[Row]) is more useful than a typed dataset.
For example, when I'm consuming data without a fixed schema, like JSON files containing records of different types with different fields. Using a Dataframe I can easily "select" out the fields I need without needing to know the whole schema, or even use a runtime configuration to specify the fields I'll access.
Another consideration is that Spark can better optimize the built-in Spark SQL operations and aggregations than UDAFs and custom lambdas. So if you want to get the square root of a value in a column, that's a built-in function (df.withColumn("rootX", sqrt("X"))) in Spark SQL but doing it in a lambda (ds.map(X => Math.sqrt(X))) would be less efficient since Spark can't optimize your lambda function as effectively.
There are also many untyped Dataframe functions (like statistical functions) that are implemented for Dataframes but not typed Datasets, and you'll often find that even if you start out with a Dataset, by the time you've finished your aggregations you're left with a Dataframe because the functions work by creating new columns, modifying the schema of your dataset.
In general I don't think you should migrate from working Dataframe code to typed Datasets unless you have a good reason to. Many of the Dataset features are still flagged as "experimental" as of Spark 2.4.0, and as mentioned above not all Dataframe features have Dataset equivalents.
Limitations of Spark Datasets:
Datasets used to be less performant (not sure if that's been fixed yet)
You need to define a new case class whenever you change the Dataset schema, which is cumbersome
Datasets don't offer as much type safety as you might expect. We can pass the reverse function a date object and it'll return a garbage response rather than erroring out.
import java.sql.Date
case class Birth(hospitalName: String, birthDate: Date)
val birthsDS = Seq(
Birth("westchester", Date.valueOf("2014-01-15"))
).toDS()
birthsDS.withColumn("meaningless", reverse($"birthDate")).show()
+------------+----------+-----------+
|hospitalName| birthDate|meaningless|
+------------+----------+-----------+
| westchester|2014-01-15| 51-10-4102|
+------------+----------+-----------+
Related
I started reading the book called "Spark definitive guide-big data processing made simple" to learn Spark. While I was reading I saw a line saying "A DataFrame is the most common Structured API and simply represents a table of data with rows and columns." I am not able to understand why are RDDs and DataFrames being called APIs?
They're called APIs because they're essentially just different interfaces to exactly the same data. DataFrame can be built on top of RDD and RDD can be extracted from DataFrame. They just have different sets of functions defined on that data, main differences are semantics and the way you work with data, RDD being lower level API and DataFrame being higher level API. For example you can use Spark SQL interface with DataFrame which provides all common SQL functions, but if you decide to use RDDs, you would need to write SQL functions yourself using RDD transformations.
And of course, they both exist because it really comes down to your use case.
I am trying to process data from Kafka using Spark Structured Streaming. The code for ingesting the data is as follows:
val enriched = df.select($"value" cast "string" as "json")
.select(from_json($"json", schema) as "data")
.select("data.*")
ds is a DataFrame with the data consumed from Kafka.
The problem comes when I try to read is as JSON in order to do faster queries. the function that comes from org.apache.spark.sql.functions from_json() is asking obligatory for a schema. What if the messages have some different fields?
As #zero323 and the answer he or she referenced suggest, you are asking a contradictory question: essentially how does one impose a schema when one doesn't know the schema? One can't of course. I think the idea to use open-ended collection types is your best option.
Ultimately though, it is almost certainly true that you can represent your data with a case class even if it means using a lot of Options, strings you need to parse, and maps you need to interrogate. Invest in the effort to define that case class. Otherwise, your Spark jobs will essentially a lot of ad hoc, time-consuming busywork.
I have a very big table of time series data that have these columns:
Timestamp
LicensePlate
UberRide#
Speed
Each collection of LicensePlate/UberRide data should be processed considering the whole set of data. In others words, I do not need to proccess the data row by row, but all rows grouped by (LicensePlate/UberRide) together.
I am planning to use spark with dataframe api, but I am confused on how can I perform a custom calculation over spark grouped dataframe.
What I need to do is:
Get all data
Group by some columns
Foreach spark dataframe group apply a f(x). Return a custom object foreach group
Get the results by applying g(x) and returning a single custom object
How can I do steps 3 and 4? Any hints over which spark API (dataframe, dataset, rdd, maybe pandas...) should I use?
The whole workflow can be seen below:
What you are looking for exists since Spark 2.3: Pandas vectorized UDFs. It allows to group a DataFrame and apply custom transformations with pandas, distributed on each group:
df.groupBy("groupColumn").apply(myCustomPandasTransformation)
It is very easy to use so I will just put a link to Databricks' presentation of pandas UDF.
However, I don't know such a practical way to make grouped transformations in Scala yet, so any additional advice is welcome.
EDIT: in Scala, you can achieve the same thing since earlier versions of Spark, using Dataset's groupByKey + mapGroups/flatMapGroups.
While Spark provides some ways to integrate with Pandas it doesn't make Pandas distributed. So whatever you do with Pandas in Spark is simply local (either to driver or executor when used inside transformations) operation.
If you're looking for a distributed system with Pandas-like API you should take a look at dask.
You can define User Defined Aggregate functions or Aggregators to process grouped Datasets but this part of the API is directly accessible only in Scala. It is not that hard to write a Python wrapper when you create one.
RDD API provides a number of functions which can be used to perform operations in groups starting with low level repartition / repartitionAndSortWithinPartitions and ending with a number of *byKey methods (combineByKey, groupByKey, reduceByKey, etc.).
Which one is applicable in your case depends on the properties of the function you want to apply (is it associative and commutative, can it work on streams, does it expect specific order).
The most general but inefficient approach can be summarized as follows:
h(rdd.keyBy(f).groupByKey().mapValues(g).collect())
where f maps from value to key, g corresponds to per-group aggregation and h is a final merge. Most of the time you can do much better than that so it should be used only as the last resort.
Relatively complex logic can be expressed using DataFrames / Spark SQL and window functions.
See also Applying UDFs on GroupedData in PySpark (with functioning python example)
I have a MySQL database with a single table containing about 100 million records (~25GB, ~5 columns). Using Apache Spark, I extract this data via a JDBC connector and store it in a DataFrame.
From here, I do some pre-processing of the data (e.g. replacing the NULL values), so I absolutely need to go through each record.
Then I would like to perform dimensionality reduction and feature selection (e.g. using PCA), perform clustering (e.g. K-Means) and later on do the testing of the model on new data.
I have implemented this in Spark's Java API, but it is too slow (for my purposes) since I do a lot of copying of the data from a DataFrame to a java.util.Vector and java.util.List (to be able to iterate over all records and do the pre-processing), and later back to a DataFrame (since PCA in Spark expects a DataFrame as input).
I have tried extracting information from the database into a org.apache.spark.sql.Column but cannot find a way to iterate over it.
I also tried avoiding the use of Java data structures (such as List and Vector) by using the org.apache.spark.mllib.linalg.{DenseVector, SparseVector}, but cannot get that to work either.
Finally, I also considered using JavaRDD (by creating it from a DataFrame and a custom schema), but couldn't work it out entirely.
After a lengthy description, my question is: is there a way to do all steps mentioned in the first paragraph, without copying all the data into a Java data structure?
Maybe one of the options I tried could actually work, but I just can't seem to find out how, as the docs and literature on Spark are a bit scarce.
From the wording of your question, it seems there is some confusion about the stages of Spark processing.
First, we tell Spark what to do by specifying inputs and transformations. At this point, the only things that are known are (a) the number of partitions at various stages of processing and (b) the schema of the data. org.apache.spark.sql.Column is used at this stage to identify the metadata associated with a column. However, it doesn't contain any of the data. In fact, there is no data at all at this stage.
Second, we tell Spark to execute an action on a dataframe/dataset. This is what kicks off processing. The input is read and flows through the various transformations and into the final action operation, be it collect or save or something else.
So, that explains why you cannot "extract information from the database into" a Column.
As for the core of your question, it's hard to comment without seeing your code and knowing exactly what it is you are trying to accomplish but it is safe to say that much migrating between types is a bad idea.
Here are a couple of questions that might help guide you to a better outcome:
Why can't you perform the data transformations you need by operating directly on the Row instances?
Would it be convenient to wrap some of your transformation code into a UDF or UDAF?
Hope this helps.
Spark SQL DataFrame/Dataset execution engine has several extremely efficient time & space optimizations (e.g. InternalRow & expression codeGen). According to many documentations, it seems to be a better option than RDD for most distributed algorithms.
However, I did some sourcecode research and am still not convinced. I have no doubt that InternalRow is much more compact and can save large amount of memory. But execution of algorithms may not be any faster saving predefined expressions. Namely, it is indicated in sourcecode of org.apache.spark.sql.catalyst.expressions.ScalaUDF, that every user defined function does 3 things:
convert catalyst type (used in InternalRow) to scala type (used in GenericRow).
apply the function
convert the result back from scala type to catalyst type
Apparently this is even slower than just applying the function directly on RDD without any conversion. Can anyone confirm or deny my speculation by some real-case profiling and code analysis?
Thank you so much for any suggestion or insight.
From this Databricks' blog article A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets
When to use RDDs?
Consider these scenarios or common use cases for
using RDDs when:
you want low-level transformation and actions and control on your
dataset;
your data is unstructured, such as media streams or streams
of text;
you want to manipulate your data with functional programming
constructs than domain specific expressions;
you don’t care about
imposing a schema, such as columnar format, while processing or
accessing data attributes by name or column;
and you can forgo some
optimization and performance benefits available with DataFrames and
Datasets for structured and semi-structured data.
In High Performance Spark's Chapter 3. DataFrames, Datasets, and Spark SQL, you can see some performance you can get with the Dataframe/Dataset API compared to RDD
And in the Databricks' article mentioned you can also find that Dataframe optimizes space usage compared to RDD
I think Dataset is schema RDD.
when you create Dataset,you should give StructType to it.
In fact, Dataset after logic plan and physical plan ,will generate RDD operator.Maybe this is RDD performance more than Dataset.