In the below  code you are impeding Spark from doing what is meant to do.
As mentioned below, the best (and easiest to implement) aproach would be to load each file into a dataframe and join between them.
Even doing a key join with RDDS would be better, but in your case you are forcing a one by one calculation.
Bentzi




On Sun, Apr 26, 2020 at 19:03, Gourav Sengupta
<gourav.sengupta@gmail.com> wrote:
Hi,

Why are you using RDDs? And how are the files stored in terms if compression? 

Regards 
Gourav

On Sat, 25 Apr 2020, 08:54 Roland Johann, <roland.johann@phenetic.io.invalid> wrote:
You can read both, the logs and the tree file into dataframes and join them. Doing this spark can distribute the relevant records or even the whole dataframe via broadcast to optimize the execution.

Best regards

Sonal Goyal <sonalgoyal4@gmail.com> schrieb am Sa. 25. Apr. 2020 um 06:59:
How does your tree_lookup_value function work?

Thanks,
Sonal
Nube Technologies 






On Fri, Apr 24, 2020 at 8:47 PM Arjun Chundiran <arjuncec@gmail.com> wrote:
Hi Team,

I have asked this question in stack overflow and I didn't really get any convincing answers. Can somebody help me to solve this issue?

Below is my problem

While building a log processing system, I came across a scenario where I need to look up data from a tree file (Like a DB) for each and every log line for corresponding value. What is the best approach to load an external file which is very large into the spark ecosystem? The tree file is of size 2GB.

Here is my scenario

  1. I have a file contains huge number of log lines.
  2. Each log line needs to be split by a delimiter to 70 fields
  3. Need to lookup the data from tree file for one of the 70 fields of a log line.

I am using Apache Spark Python API and running on a 3 node cluster.

Below is the code which I have written. But it is really slow

def process_logline(line, tree):
    row_dict = {}
    line_list = line.split(" ")
    row_dict["host"] = tree_lookup_value(tree, line_list[0])
    new_row = Row(**row_dict)
    return new_row

def run_job(vals):
    spark.sparkContext.addFile('somefile')
    tree_val = open(SparkFiles.get('somefile'))
    lines = spark.sparkContext.textFile("log_file")
    converted_lines_rdd = lines.map(lambda l: process_logline(l, tree_val))
    log_line_rdd = spark.createDataFrame(converted_lines_rdd)
    log_line_rdd.show()
Basically I need some option to load the file one time in memory of workers and start using it entire job life time using Python API.
Thanks in advance
Arjun



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