Process very big csv file without timeout and memory error

At the moment I'm writing an import script for a very big CSV file. The Problem is most times it stops after a while because of an timeout or it throws an memory error.

My Idea was now to parse the CSV file in "100 lines" steps and after 100 lines recall the script automatically. I tried to achieve this with header (location ...) and pass the current line with get but it didn't work out as I want to.

Is there a better way to this or does someone have an idea how to get rid of the memory error and the timeout?


I've used fgetcsv to read a 120MB csv in a stream-wise-manner (is that correct english?). That reads in line by line and then I've inserted every line into a database. That way only one line is hold in memory on each iteration. The script still needed 20 min. to run. Maybe I try Python next time… Don't try to load a huge csv-file into an array, that really would consume a lot of memory.

// WDI_GDF_Data.csv (120.4MB) are the World Bank collection of development indicators:
// http://data.worldbank.org/data-catalog/world-development-indicators
if(($handle = fopen('WDI_GDF_Data.csv', 'r')) !== false)
{
    // get the first row, which contains the column-titles (if necessary)
    $header = fgetcsv($handle);

    // loop through the file line-by-line
    while(($data = fgetcsv($handle)) !== false)
    {
        // resort/rewrite data and insert into DB here
        // try to use conditions sparingly here, as those will cause slow-performance

        // I don't know if this is really necessary, but it couldn't harm;
        // see also: http://php.net/manual/en/features.gc.php
        unset($data);
    }
    fclose($handle);
}

Process very big csv file without timeout and memory error, very big CSV file. The Problem is most times it stops after a while because of an timeout or it throws an memory error. My Idea was now to parse the CSV file in​  TL;DR version: Is it efficient to save the last line I'm currently on of a CSV file that has 38K lines of products then, and after X number of rows, reset the connection and start from where I left off? Or is there another way to parse a Large CSV file without timeouts? NOTE: It's the PHP script execution time. Currently at 38K lines, it takes


I find uploading the file and inserting using mysql's LOAD DATA LOCAL query a fast solution eg:

    $sql = "LOAD DATA LOCAL INFILE '/path/to/file.csv' 
        REPLACE INTO TABLE table_name FIELDS TERMINATED BY ',' 
        ENCLOSED BY '\"' LINES TERMINATED BY '\r\n' IGNORE 1 LINES";
    $result = $mysqli->query($sql);

PHP script timeout while reading large file - PHP, At the moment I'm writing an import script for a very big CSV file. The Problem is most times it stops after a while because of an timeout or it throws an memory  My issue comes when I have a large CSV file to work with (approx 220Mb with ~500k lines). The script takes a very long time to run. I appreciate that it's going to take a while to process that much data, but I'm wondering if there is a way to improve it! Here is the condensed script:


If you don't care about how long it takes and how much memory it needs, you can simply increase the values for this script. Just add the following lines to the top of your script:

ini_set('memory_limit', '512M');
ini_set('max_execution_time', '180');

With the function memory_get_usage() you can find out how much memory your script needs to find a good value for the memory_limit.

You might also want to have a look at fgets() which allows you to read a file line by line. I am not sure if that takes less memory, but I really think this will work. But even in this case you have to increase the max_execution_time to a higher value.

How to Read Big Files with PHP (Without Killing Your Server , I'm trying to write a script to import a CSV file into my database. I suggests that set_time_limit just adds (or not if set to 0) the timeout value set in itself (then use fseek() to move to the position given by $_GET['fseek]) so that script execution would memory_limit = 16M ; Maximum amount of memory a script may consume How to append several large data.table objects into a single data.table and export to csv quickly without running out of memory? 7 Loading data bigger than the memory size in h2o


There seems to be an enormous difference between fgetcsv() and fgets() when it comes to memory consumption. A simple CSV with only one column passed my 512M memory limit for just 50000 records with fgetcsv() and took 8 minutes to report that.

With fgets() it took only 3 minutes to successfully process 649175 records, and my local server wasn't even gasping for additional air..

So my advice is to use fgets() if the number of columns in your csv is limited. In my case fgets() returned directly the string inside column 1. For more then one column, you might use explode() in a disposable array which you unset() after each record operation. Thumbed up answer 3 @ndkauboy

Reading large File increases Execution time & Memory (E.g file with , Christopher Pitt shows how to read and write large files efficiently, It's not often that we, as PHP developers, need to worry about memory management. How fast or slow is the process we want to work on? We could also want to transform a stream of data without ever really needing access to the data. First of all unzipped the file to a ~4GB .csv-file. Then: import pandas as pd train = pd.read_csv("train.csv") Result: Machine frozen and starting to swap. My RAM (16GB) is completely filled by this data frame which I didn't expect but well, it's feasible… Maybe you have some similar problem.


Oh. Just make this script called as CLI, not via silly web interface. So, no execution time limit will affect it. And do not keep parsed results forever but write them down immediately - so, you won't be affected by memory limit either.

Streaming large CSV files with Laravel chunked queries, Try loading csv file of 500000 records and split it into smaller set of files of about So we wrote our own adaptor that stores several rows per file instead. this file to CSV, we are able to process the entire thing without any  Normally when working with CSV data, I read the data in using pandas and then start munging and analyzing the data. With files this large, reading the data into pandas directly can be difficult (or impossible) due to memory constrictions, especially if you’re working on a prosumer computer.


High-volume troubleshooting, });. This will keep your memory within limits. Some tips. Remember that you still have to check your execution time, so you  From my experience, reading the file by chunks of 1000 or 10000 lines usually gives the best results and has low to no impact on CPU and memory even for such a large logfile. The problem with -ReadCount is that it takes a bit of guessing to find out the right value for a specific file, since performance varies not only with the size of the file but also with the size of each record.


Parse CSV file chunk by chunk and save in database, Find answers to common questions about memory inefficiencies in the Atom. Why am I getting an OutOfMemoryException error while attempting to process You are attempting to process a single large data file without batching the data. To process large CSV files, use the Data Process shape with Split Documents to​  After that, the 6.4 gig CSV file processed without any issues. Creating Large XML Files in Python. This part of the process, taking each row of csv and converting it into an XML element, went fairly smoothly thanks to the xml.sax.saxutils.XMLGenerator class. The API for creating elements isn't an example of simplicity, but it is--unlike many of the more creative schemes--predictable, and has one killer feature: it correctly writes output to a stream.


Loading data from a local data source | BigQuery, In this article I will demonstrate how to read a large CSV file chunk by chunk (line basis) I used and face challenges like memory management, performance, large file Client will upload very large csv type data file by their web application​. If destination table has any column which is not mentioned in source data then  Tired of getting Memory Errors while trying to read very big (more than 1 GB) CSV files to Python? This is a common case when you download a very rich dataset from Kaggle.com and try to load the