get specific row from spark dataframe

Question

Is there any alternative for df[100, c(\"column\")] in scala spark data frames. I want to select specific row from a column of spark data frame. for example

Answer 1

The getrows() function below should get the specific rows you want.

For completeness, I have written down the full code in order to reproduce the output.

# Create SparkSession
from pyspark.sql import SparkSession
spark = SparkSession.builder.master('local').appName('scratch').getOrCreate()

# Create the dataframe
df = spark.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["letter", "name"])

# Function to get rows at `rownums`
def getrows(df, rownums=None):
    return df.rdd.zipWithIndex().filter(lambda x: x[1] in rownums).map(lambda x: x[0])

# Get rows at positions 0 and 2.
getrows(df, rownums=[0, 2]).collect()

# Output:
#> [(Row(letter='a', name=1)), (Row(letter='c', name=3))]

Answer 2

This is how I achieved the same in Scala. I am not sure if it is more efficient than the valid answer, but it requires less coding

val parquetFileDF = sqlContext.read.parquet("myParquetFule.parquet")

val myRow7th = parquetFileDF.rdd.take(7).last

Answer 3

In PySpark, if your dataset is small (can fit into memory of driver), you can do

df.collect()[n]

where df is the DataFrame object, and n is the Row of interest. After getting said Row, you can do row.myColumn or row["myColumn"] to get the contents, as spelled out in the API docs.

Answer 4

When you want to fetch max value of a date column from dataframe, just the value without object type or Row object information, you can refer to below code.

table = "mytable"

max_date = df.select(max('date_col')).first()[0]

2020-06-26
instead of Row(max(reference_week)=datetime.date(2020, 6, 26))

Answer 5

There is a scala way (if you have a enough memory on working machine):

val arr = df.select("column").rdd.collect
println(arr(100))

If dataframe schema is unknown, and you know actual type of "column" field (for example double), than you can get arr as following:

val arr = df.select($"column".cast("Double")).as[Double].rdd.collect

Answer 6

Following is a Java-Spark way to do it , 1) add a sequentially increment columns. 2) Select Row number using Id. 3) Drop the Column

import static org.apache.spark.sql.functions.*;
..

ds = ds.withColumn("rownum", functions.monotonically_increasing_id());
ds = ds.filter(col("rownum").equalTo(99));
ds = ds.drop("rownum");

N.B. monotonically_increasing_id starts from 0;