{"id":888,"date":"2022-02-03T14:58:18","date_gmt":"2022-02-03T14:58:18","guid":{"rendered":"https:\/\/zerotobyte.com\/?p=888"},"modified":"2022-02-03T15:38:17","modified_gmt":"2022-02-03T15:38:17","slug":"using-django-bulk-create-and-bulk-update","status":"publish","type":"post","link":"https:\/\/zerotobyte.com\/using-django-bulk-create-and-bulk-update\/","title":{"rendered":"Using Django bulk_create and bulk_update"},"content":{"rendered":"\n<p>Even though Django is known for the fast and comfortable development of web applications, sometimes performance can become an issue. There can be a lot of bottlenecks, and Django often has tools to prevent it.&nbsp;<\/p>\n\n\n\n<p><strong>One of the most time-consuming processes in web development is accessing the database. However, many performance improvements can be made with minor changes using built-in tools.<\/strong><\/p>\n\n\n\n<p>In this article, I\u2019ll cover two known methods for basic optimization:<\/p>\n\n\n\n<ul><li><strong><code>bulk_create<\/code><\/strong><\/li><li><strong><code>bulk_update<\/code><\/strong><\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"django-bulk-create\">Django bulk_create<\/h2>\n\n\n\n<p><strong>Django <code>bulk_create<\/code> can help us optimize our application using a small number of database calls to save a lot of data. <\/strong>In other words, <code>bulk_create<\/code> can save multiple model instances into the database using only one database call.<\/p>\n\n\n\n<p><code><strong>bulk_create(objs, batch_size=None, ignore_conflicts=False)<\/strong><\/code><\/p>\n\n\n\n<p><code>bulk_create<\/code> requires one argument and has two default non-required arguments:<\/p>\n\n\n\n<ul><li><code>objs<\/code> &#8211; List of objects that should be created.<\/li><li><code>batch_size<\/code> &#8211; Defines how many instances are created with a single database call. Defaults None.<\/li><li><code>ignore_conflicts<\/code> &#8211; Ignores any insert failures. Defaults False.<\/li><\/ul>\n\n\n\n<pre class=\"wp-block-verse\"><strong>WARNING<\/strong> - If ignore_conflicts is set to True, setting the primary key on each model instance is disabled.\nOracle database doesn't support ignore_conflicts=True.<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"django-bulk-create-example\">Django bulk_create example<\/h3>\n\n\n\n<p>In <code>bulk_create<\/code> and <code>bulk_update<\/code>, I&#8217;ll use the same Post model. It&#8217;s a simple model containing only two arguments, title and time_published.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">class Post(models.Model):\n    title = models.CharField(max_length=512)\n    time_published = models.DateTimeField(auto_now_add=True)<\/pre>\n\n\n\n<p>If we were to create three posts the standard way, it would look like this:<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">Post.objects.create(title=\"Django bulk_create example 1.0\")\nPost.objects.create(title=\"Django bulk_create example 2.0\")\nPost.objects.create(title=\"Django bulk_create example 3.0\")<\/pre>\n\n\n\n<p>Here we are accessing the database three times. Let&#8217;s see the same example using the bulk method.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">Post.objects.bulk_create(\n    [Post(title='Django bulk_create example 1.0'),\n     Post(title='Django bulk_create example 2.0'), \n     Post(title='Django bulk_create example 3.0')\n])<\/pre>\n\n\n\n<p><strong>Only one database call is used to create these three instances of Post using <code>bulk_create<\/code>. <\/strong>How much time can we save, and is <code>bulk_create<\/code> even faster than the standard create method? I&#8217;ve done a detailed analysis in the next chapter.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"how-much-is-bulk-create-actually-faster\">How much is bulk create actually faster?<\/h3>\n\n\n\n<p>In this section, we&#8217;ll test the time performance of <code>bulk_create<\/code>, comparing it to standard (one by one) way of inserting data into the database.<\/p>\n\n\n\n<pre class=\"wp-block-verse\"><strong>NOTE<\/strong> - For time analysis, I used the cProfile module detailed explained in the <a href=\"https:\/\/docs.python.org\/3\/library\/profile.html#module-cProfile\" target=\"_blank\" rel=\"noreferrer noopener\">official Python documentation<\/a>.<\/pre>\n\n\n\n<p><strong>In the following examples, we&#8217;ll test the performance of inserting 1000 rows into the database.<\/strong><\/p>\n\n\n\n<p>First, let&#8217;s check out the standard method:<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">def without_bulk():\n    for i in range(1000):\n        Post.objects.create(title=f\"Post {i}\")\n\np = cProfile.Profile()\np.runcall(without_bulk)\np.print_stats(sort='tottime')<\/pre>\n\n\n\n<p>Running profilers on a given method, we get this output:<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">266262 function calls (261255 primitive calls) in 4.121 seconds\n\n   Ordered by: internal time\n\n   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n     1001    2.721    0.003    2.740    0.003 {method 'execute' of 'psycopg2.extensions.cursor' objects}\n     3000    0.070    0.000    0.079    0.000 query.py:151(__init__)\n     1000    0.060    0.000    0.306    0.000 compiler.py:1371(as_sql)\n     1000    0.057    0.000    0.076    0.000 base.py:406(__init__)\n     1000    0.054    0.000    3.520    0.004 compiler.py:1432(execute_sql)\n     2000    0.048    0.000    0.068    0.000 utils.py:105(debug_sql)<\/pre>\n\n\n\n<p><strong>More than half of the time was spent on the <code>execute<\/code> method of the psycopg2 library<\/strong>. Every time we saved Post, execute method needs to be called, obviously that&#8217;s a bottleneck that can be avoided.<\/p>\n\n\n\n<p>Let&#8217;s try out <code>bulk_create<\/code>:<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">def with_bulk():\n    Post.objects.bulk_create([Post(title=f\"Post {i}\") for i in range(1000)])\n\np = cProfile.Profile()\np.runcall(with_bulk)\np.print_stats(sort='tottime')<\/pre>\n\n\n\n<p>Running profilers on the bulk method, we get this output:<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">83591 function calls (82572 primitive calls) in 0.177 seconds\n\n   Ordered by: internal time\n\n   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n        1    0.042    0.042    0.043    0.043 {built-in method psycopg2._psycopg._connect}\n     1000    0.037    0.000    0.051    0.000 base.py:406(__init__)\n        2    0.020    0.010    0.020    0.010 {method 'execute' of 'psycopg2.extensions.cursor' objects}\n        1    0.006    0.006    0.018    0.018 query.py:460(_prepare_for_bulk_create)\n        1    0.006    0.006    0.057    0.057 views.py:10(&lt;listcomp>)\n     3000    0.005    0.000    0.007    0.000 __init__.py:845(get_default)<\/pre>\n\n\n\n<p><strong>It took a standard way more than 3.944 seconds more to save the same amount (1000) model instances to the database. That&#8217;s almost 25 times slower than bulk_create.<\/strong> So obviously, we made some performance improvements.<\/p>\n\n\n\n<p>Let\u2019s check out bulk_update now.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"django-bulk-update\">Django bulk_update<\/h2>\n\n\n\n<p><strong>Django <code>bulk_update<\/code> has the same goal as bulk_create, which is to minimize the number of queries to the database.<\/strong> The only difference is that now we are updating given objects instead of inserting them into the database.<\/p>\n\n\n\n<p><strong><code>bulk_update(objs, fields, batch_size=None)<\/code><\/strong><\/p>\n\n\n\n<p><code>bulk_update<\/code> requires two arguments and has one default non-required argument:<\/p>\n\n\n\n<ul><li><code>objs<\/code> &#8211; List of objects that should be created.<\/li><li><code>fields<\/code> &#8211; Fields that should be updated.<\/li><li><code>batch_size<\/code> &#8211; Defines how many instances are updated with a single database call. Defaults None.<\/li><\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"django-bulk-update-example\">Django bulk_update example<\/h3>\n\n\n\n<p>We\u2019ll use the same Post model used in the bulk_create example.<\/p>\n\n\n\n<p>As seen below:<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">class Post(models.Model):\n    title = models.CharField(max_length=512)\n    time_published = models.DateTimeField(auto_now_add=True)<\/pre>\n\n\n\n<p>Considering we already have our 1000 Posts from bulk_create example, we\u2019ll change (update) the title of every single one, making it 1000 database calls once again:<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">def without_bulk():\n    posts = list(Post.objects.all())\n    for index, post in enumerate(posts):\n        posts[index].title = f\"Post {index}\"\n        posts[index].save()\n\np = cProfile.Profile()\np.runcall(without_bulk)\np.print_stats(sort='tottime')<\/pre>\n\n\n\n<p>We got the following results:<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">451700 function calls (446664 primitive calls) in 4.312 seconds\n\n   Ordered by: internal time\n\n   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n     1002    2.577    0.003    2.591    0.003 {method 'execute' of 'psycopg2.extensions.cursor' objects}\n     3000    0.060    0.000    0.109    0.000 query.py:292(clone)\n     1000    0.053    0.000    0.278    0.000 query.py:1221(build_filter)\n     1000    0.041    0.000    4.235    0.004 base.py:747(save_base)\n     2002    0.041    0.000    0.057    0.000 utils.py:105(debug_sql)\n     1000    0.039    0.000    0.401    0.000 compiler.py:1521(as_sql)<\/pre>\n\n\n\n<p>Let\u2019s now see bulk_update:<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">def with_bulk():\n    posts = list(Post.objects.all())\n    for index, post in enumerate(posts):\n        posts[index].title = f\"Post {index}\"\n\n    Post.objects.bulk_update(posts, ['title'])\n\np = cProfile.Profile()\np.runcall(with_bulk)\np.print_stats(sort='tottime')<\/pre>\n\n\n\n<p>And we get these results:<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">437459 function calls (384393 primitive calls) in 0.477 seconds\n\n   Ordered by: internal time\n\n   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n        3    0.037    0.012    0.037    0.012 {method 'execute' of 'psycopg2.extensions.cursor' objects}\n     6007    0.024    0.000    0.096    0.000 copy.py:66(copy)\n     1001    0.016    0.000    0.080    0.000 query.py:1221(build_filter)\n26068\/26062    0.013    0.000    0.013    0.000 {built-in method builtins.getattr}\n     6007    0.013    0.000    0.038    0.000 copy.py:258(_reconstruct)\n11023\/1017    0.012    0.000    0.054    0.000 functional.py:40(__get__)<\/pre>\n\n\n\n<p><strong>Once again, we saved around 3.83 seconds only by implementing the built-in Django tool making our code 10 times faster.<\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"results-summary\">Results summary<\/h2>\n\n\n\n<p>This short chapter will cover all-time results obtained from creating and updating 1000 objects using bulk and standard methods.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Method used<\/strong><\/td><td><strong>Seconds<\/strong><\/td><\/tr><tr><td>bulk_create<\/td><td>0.177<\/td><\/tr><tr><td>bulk_update<\/td><td>0.477<\/td><\/tr><tr><td>create<\/td><td>4.121<\/td><\/tr><tr><td>update<\/td><td>4.312<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<pre class=\"wp-block-verse\">Django 4.0.1\nPython 3.8.10\nPostgreSQL 14.1\n\nSystem specs:\n\nCPU: Intel\u00ae Core\u2122 i5-7200U CPU @ 2.50GHz \u00d7 4\nGPU: NV117 \/ Mesa Intel\u00ae HD Graphics 620\nRAM: 8GB<\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Even though Django is known for the fast and comfortable development of web applications, sometimes performance can become an issue. &#8230; 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