serpy | ridiculously fast object serialization

 by   clarkduvall Python Version: Current License: MIT

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ridiculously fast object serialization
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            Community Discussions

            QUESTION

            Serpy .to_represenatiaon
            Asked 2019-Jan-12 at 15:38

            Does serpy have an equivalent .to_representation like in DRF

            ...

            ANSWER

            Answered 2019-Jan-12 at 15:38

            Serpy has the .to_value method that can be overridden to perform custom serialization akin to DRFs .to_representation.

            You'll likely want to call the base class first within your .to_value method.

            Source https://stackoverflow.com/questions/54152184

            QUESTION

            How to do performance optimization while serializing lots of GeoDjango geometry fields?
            Asked 2018-Jan-03 at 15:20

            I'm developing a GeoDjango app which use the provided WorldBorder model in the tutorial. I also created my own Region model which is tied to WorldBorder. So a WorldBorder/Country can have multiple Regions which has borders (MultiPolygon field) in it too.

            I made the API for it using DRF but it's so slow, it takes 16 seconds to load all WorldBorder and Regions in GeoJSON format. The returned JSON size is 10MB though. Is that reasonable?

            I even change the serializer to serpy which is way much faster than the DRF GIS serializer but only offers 10% performance improvement.

            Turns out after profiling, most of the time is spent in the GIS functions to convert data type in the database to list of coordinates instead of WKT. If I use WKT, the serialization is much faster (1.7s compared to 11.7s, the WKT is only for WorldBorder MultiPolygon, everything else is still in GeoJson)

            I also tried to compress the MultiPolygon using ST_SimplifyVW with low tolerance (0.005) to preserve the accuracies, which brings down the JSON size to 1.7 MB. This makes the total load to 3.5s. Of course I can still find which is the best tolerance to balance accuracy and speed.

            Below is the profiling data (the sudden increase of queries in the simplified MultiPolygon is due to bad usage of Django QS API to get use of ST_SimplifyVW)

            EDIT: I fixed the DB query so the query calls stays the same at 75 queries and as expected, it does not increase the performance significantly.

            EDIT: I continued to improve my DB queries. I reduced it to just 8 queries now. As expected, it does not improve that much performance.

            Below is profiling for the function calls. I highlight the part which took most of the time. This one is using vanilla DRF GIS implementation.

            Below is when I use WKT for one of the MultiPolygon field without ST_SimplifyVW.

            Here's the models as requested by @Udi

            ...

            ANSWER

            Answered 2018-Jan-01 at 20:08

            Since your geographic data does not change frequently, try caching all region/country polygons in pre-calculated geojsons. I.e., create a /country/123.geojson API call or static file with the geo data for all regions in this country, probably simplified in advance.

            Your other API calls should return only the numeric data, without geographic polygons, leaving the combining task to the client.

            Source https://stackoverflow.com/questions/48040545

            QUESTION

            How to generate and output multiple java objects using spark?
            Asked 2017-Dec-14 at 15:57

            I have a CSV on the hadoop file system hdfs that I want to convert into multiple serialized java objects using this framework:

            ...

            ANSWER

            Answered 2017-Dec-14 at 15:57

            The output objects should be readable by a normal non-hadoop/spark related Java program

            For that to work you will need to save your results outside of HDFS. So what you could do is:

            • Read the CSV data from HDFS using SparkContext.textFile in Spark
            • Grab a limited number of rows into your driver using RDD.take()
              • The argument here will be the number of rows you want e.g. myRdd.take(1000) to grab 1000 rows
            • myRdd.collect() will grab everything, but if you have a lot of data, that can cause an OutOfMemoryError on your spark driver
            • Now you will have all the rows as an array, you can store them using a basic java serializer

            Sample Code:

            Source https://stackoverflow.com/questions/47805327

            QUESTION

            Possible Bug on Django 1.11 with multitable Inheritance or possible bug on python 3.5 or am i?
            Asked 2017-Jul-02 at 22:16

            Using python 3.5.2 and django 1.11 multitable inheritance like this:

            ...

            ANSWER

            Answered 2017-Jul-02 at 20:48

            This has nothing to do with the manager, or indeed with Django at all. A list comprehension always constructs a list; that is its main purpose. But the only thing you're doing inside that list comp is calling print(), which returns None. So the result of that entire expression is a list containing a single None, which the shell helpfully prints for you.

            Really, you should not use list comprehensions for their side effects. Use a proper loop.

            Source https://stackoverflow.com/questions/44875055

            QUESTION

            Django: Serializing a list of multiple, chained models
            Asked 2017-Jan-19 at 14:52

            Given two different models, with the same parent base class. Is there any way, using either Django Rest Framework Serializers or serpy, to serialize a chained list containing instances of both the child models?

            Given some example models:

            ...

            ANSWER

            Answered 2017-Jan-19 at 14:45

            From your exception I suppose problem with a BaseModelSerializer because it have both fields from both models. I think your better write a two separate serializers for each models and then sort output from them by common field:

            Source https://stackoverflow.com/questions/41744319

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