I'm in the process of migrating current DataBricks Spark notebooks to Jupyter notebooks, DataBricks provides convenient and beautiful display(data_frame) function to be able to visualize Spark dataframes and RDDs ,but there's no direct equivalent for Jupyter(im not sure but i think its a DataBricks specific function), i tried :
dataframe.show()
But it's a text version of it ,when you have many columns it breaks , so i'm trying to find an alternative to display() that can render Spark dataframes better than show() functions. Is there any equivalent or alternative to this?
When you use Jupyter, instead of using df.show() use myDF.limit(10).toPandas().head(). And, as sometimes, we are working multiple columns it truncates the view.
So just set your Pandas view column config to the max.
# Alternative to Databricks display function.
import pandas as pd
pd.set_option('max_columns', None)
myDF.limit(10).toPandas().head()
First Recommendation: When you use Jupyter, don't use df.show() instead use df.limit(10).toPandas().head() which results perfect display even better Databricks display()
Second Recommendation:
Zeppelin Notebook. Just use z.show(df.limit(10))
Additionally in Zeppelin;
You register your dataframe as SQL Table df.createOrReplaceTempView('tableName')
Insert new paragraph beginning %sql then query your table with amazing display.
In recent IPython, you can just use display(df) if df is a panda dataframe, it will just work. On older version you might need to do a from IPython.display import display. It will also automatically display if the result of the last expression of a cell is a data_frame. For example this notebook. Of course the representation will depends on the library you use to make your dataframe. If you are using PySpark and it does not defined a nice representation by default, then you'll need to teach IPython how to display the Spark DataFrame. For example here is a project that teach IPython how to display Spark Contexts, and Spark Sessions.
Without converting to pandas dataframe. Use this... This will render dataframe in proper grids.
from IPython.core.display import HTML
display(HTML("<style>pre { white-space: pre !important; }</style>"))
df.show()
You can set config spark.conf.set('spark.sql.repl.eagerEval.enabled', True).
This will allow to display native pyspark DataFrame without explicitly using df.show() and there is also no need to transfer DataFrame to Pandas either, all you need to is just df.
Try Apache Zeppelin (https://zeppelin.apache.org/). There's some nice standard visualizations of dataframes, specifically if you use the sql interpreter. There's also support for other useful interpreters as well.
Related
I am using the below code to compare 2 columns in data frame. I dont want to do it in pandas. Can someone help how to compare using spark data frames?
df1=context.spark.read.option("header",True).csv("./test/input/test/Book1.csv",)
df1=df1.withColumn("Curated", dataclean.clean_email(col("email")))
df1.show()
assert_array_almost_equal(df1['expected'], df1['Curated'],verbose=True)
You can either do it through:
pyspark-test library which is inspired by the pandas testing module built for Spark as in this documentation or
exceptAll as in this documentation. Once used, you then have to check whether count is greater then zero, if yes, then the tables are not the same.
Good luck!
One efficient way would be to try to identify the first difference as soon as possible. One way to achieve that is via left-anti joins:
assert(df1.join(df1, (df1['expected'] == df1['Curated']), "leftanti").first() != None)
there.
I am very new to Pyspark and I am learning the UDF myself. I realize UDF sometimes will slow down your code. I want to know about your experience. What UDF function did you apply(cannot be achieved with Pyspark code only). Is there any useful UDF function that helps you clean the data? Except for the Pyspark document, is there any source that can help me learn the UDF function?
You can find most of your needed functionality within the standard library functions spark has.
import pyspark.sql.functions - Check the docs here https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.functions
Now, sometimes you do have to create custom UDF's but be aware that it does slow down since spark has to evaluate it for every dataframe row.
try to avoid this as much as you can.
When you don't have any other option, use it, but try to minimize the complexity and the external libraries you use.
Another approach is to use an RDD, which means you convert your dataframe to an rdd (MYDF.rdd)
And right after you call mapPartitions or map which accept a function that manipulate your data.
It basically sends chunks each time as a list of spark Row entity.
Read more about mapPartitions or map here: https://sparkbyexamples.com/spark/spark-map-vs-mappartitions-transformation/
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|
+------------+----------+-----------+
Is there an easy way to convert an RDD to a Dataset (or DataFrame) in Mobius. Basically something similar to the functionality provided by scala's
import sqlContext.implicits._
I know there's sqlContext.CreateDataFrame() but as far as I can tell that requires me to define my own StructType in order to do the conversion.
No. For now, sqlContext.CreateDataFrame is the only option. Feel free to create an issue in Mobius repo to get the discussion started if you think ToDF() is required on RDDs.
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)