After some extensive research I have figured that
Parquet is a column-oriented data file format designed for efficient data storage and retrieval. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk.
However, I am unable to understand why parquet writes multiple files when I run df.write.parquet("/tmp/output/my_parquet.parquet") despite supporting flexible compression options and efficient encoding.
Is this directly related to parallel processing or similar concepts?
Lots of frameworks make use of this multi-file layout feature of the parquet format. So I’d say that it’s a standard option which is part of the parquet specification, and spark uses it by default.
This does have benefits for parallel processing, but also other use cases, such as processing (in parallel or series) on the cloud or networked file systems, where data transfer times may be a significant portion of total IO. in these cases, the parquet “hive” format, which uses small metadata files which provide statistics and information about which data files to read, offers significant performance benefits when reading small subsets of the data. This is true whether a single-threaded application is reading a subset of the data or if each worker in a parallel process is reading a portion of the whole.
It's not just for parquet but rather a spark feature where to avoid network io it writes each shuffle partition as a 'part...' file on disk and each file as you said will have compression and efficient encoding by default.
So Yes it is directly related to parallel processing
Related
I'm looking at some machine learning/forecasting code using Keras, and the input data sets are stored in npz files instead of the usual csv format.
Why would the authors go with this format instead of csv? What advantages does it have?
It depends of the expected usage. If a file is expected to have broad use cases including direct access from an ordinary client machines, then csv is fine because it can be directly loaded in Excel or LibreOffice calc which are widely deployed. But it is just an good old text file with no indexes nor any additional feature.
On the other hand is a file is only expected to be used by data scientists or generally speaking numpy aware users, then npz is a much better choice because of the additional features (compression, lazy loading, etc.)
Long story made short, you exchange a larger audience for higher features.
From https://kite.com/python/docs/numpy.lib.npyio.NpzFile
A dictionary-like object with lazy-loading of files in the zipped archive provided on construction.
So, it is a zipped archive (smaller size than CSV on the disk, more than one file can be stored) and files can be loaded from disk only when needed (in CSV, when you only need 1 column, you still have to read whole file to parse it).
=> advantages are: performance and more features
I have a large python dictionary of values (around 50 GB), and I've stored it as a JSON file. I am having efficiency issues when it comes to opening the file and writing to the file. I know you can use ijson to read the file efficiently, but how can I write to it efficiently?
Should I even be using a Python dictionary to store my data? Is there a limit to how large a python dictionary can be? (the dictionary will get larger).
The data basically stores the path length between nodes in a large graph. I can't store the data as a graph because searching for a connection between two nodes takes too long.
Any help would be much appreciated. Thank you!
Although it will truly depend on what operations you want to perform on your network dataset you might want to considering storing this as a pandas Dataframe and then write it to disk using Parquet or Arrow.
That data could then be loaded to networkx or even to Spark (GraphX) for any network related operations.
Parquet is compressed and columnar and makes reading and writing to files much faster especially for large datasets.
From the Pandas Doc:
Apache Parquet provides a partitioned binary columnar serialization
for data frames. It is designed to make reading and writing data
frames efficient, and to make sharing data across data analysis
languages easy. Parquet can use a variety of compression techniques to
shrink the file size as much as possible while still maintaining good
read performance.
Parquet is designed to faithfully serialize and de-serialize DataFrame
s, supporting all of the pandas dtypes, including extension dtypes
such as datetime with tz.
Read further here: Pandas Parquet
try to use it with pandas: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_json.html
pandas.read_json(path_or_buf=None, orient=None, typ='frame', dtype=True, convert_axes=True, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, lines=False, chunksize=None, compression='infer')
Convert a JSON string to pandas object
it very lightweight and useful library to work with large data
I have a 500+ MB CSV data file. My question is, which would be faster for data manipulation (e.g., reading, processing) is the Python MySQL client would be faster since all work is mapped into SQL queries and optimization is left to the optimizer. But, at the same time Pandas is dealing with a file which should be faster than communicating with a server?
I have already checked "Large data" work flows using pandas, Best practices for importing large CSV files, Fastest way to write large CSV with Python, and Most efficient way to parse a large .csv in python?. However, I haven't really found any comparison regarding Pandas and MySQL.
Use Case:
I am working on text dataset that consists of 1,737,123 rows and 8 columns. I am feeding this dataset into RNN/LSTM network. I do some preprocessing in prior to feeding which is encoding using a customized encoding algorithm.
More details
I have 250+ experiments to do and 12 architectures (different models design) to try.
I am confused, I feel I miss something.
There's no comparison online 'cuz these two scenarios give different results:
With Pandas, you end up with a Dataframe in memory (as a NumPy ndarray under the hood), accessible as native Python objects
With MySQL client, you end up with data in a MySQL database on disk (unless you're using an in-memory database), accessible via IPC/sockets
So, the performance will depend on
how much data needs to be transferred by lower-speed channels (IPC, disk, network)
how comparatively fast is transferring vs processing (which of them is the bottleneck)
which data format your processing facilities prefer (i.e. what additional conversions will be involved)
E.g.:
If your processing facility can reside in the same (Python) process that will be used to read it, reading it directly into Python types is preferrable since you won't need to transfer it all to the MySQL process, then back again (converting formats each time).
OTOH if your processing facility is implemented in some other process and/or language, or e.g. resides within a computing cluster, hooking it to MySQL directly may be faster by eliminating the comparatively slow Python from equation, and because you'll need to be transferring the data again and converting it into the processing app's native objects anyway.
I have nearly 60-70 timing log files(all are .csv files, with a total size of nearly 100MB). I need to analyse these files at a single go. Till now, I've tried the following methods :
Merged all these files into a single file and stored it in a DataFrame (Pandas Python) and analysed them.
Stored all the csv files in a database table and analysed them.
My doubt is, which of these two methods is better? Or is there any other way to process and analyse these files?
Thanks.
For me I usually merge the file into a DataFrame and save it as a pickle but if you merge it the file will pretty big and used up a lot of ram when you used it but it is the fastest way if your machine have a lot of ram.
Storing the database is better in the long term but you will waste your time uploading the csv to the database and then waste even more of your time retrieving it from my experience you use the database if you want to query specific things from the table such as you want a log from date A to date B however if you use pandas to query all of that than this method is not very good.
Sometime for me depending on your use case you might not even need to merge it use the filename as a way to query and get the right log to process (using the filesystem) then merge the log files you are concern with your analysis only and don't save it you can save that as pickle for further processing in the future.
What exactly means analysis on a single go?
I think your problem(s) might be solved using dask and particularly the dask dataframe
However, note that the dask documentation recommends to work with one big dataframe, if it fits comfortably in the RAM of you machine.
Nevertheless, an advantage of dask might be to have a better parallelized or distributed computing support than pandas.
I'm trying to apply machine learning (Python with scikit-learn) to a large data stored in a CSV file which is about 2.2 gigabytes.
As this is a partially empirical process I need to run the script numerous times which results in the pandas.read_csv() function being called over and over again and it takes a lot of time.
Obviously, this is very time consuming so I guess there is must be a way to make the process of reading the data faster - like storing it in a different format or caching it in some way.
Code example in the solution would be great!
I would store already parsed DFs in one of the following formats:
HDF5 (fast, supports conditional reading / querying, supports various compression methods, supported by different tools/languages)
Feather (extremely fast - makes sense to use on SSD drives)
Pickle (fast)
All of them are very fast
PS it's important to know what kind of data (what dtypes) you are going to store, because it might affect the speed dramatically