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Slide 1
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PostgreSQL JSONB Performance Optimization

+

A Comprehensive Survey

+

+ 陈永源 225002025 +

+ +
+ +
+

The TOAST Threshold Problem

+ TOAST Performance Degradation +
+

The 2KB Critical Threshold

+
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  • Before TOAST: Constant access time
    • +
  • After TOAST: Linear degradation
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  • 3 additional buffer reads per access
    • +
+
+ +
+ +
+

JSONB Operator Performance

+ JSONB Operator Performance + +
+ +
+

Partial Update Challenges

+
+
+

Current Limitations

+
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  • TOAST treats JSONB as atomic BLOB
    • +
  • Full document rewrites for small changes
    • +
  • WAL write amplification
    • +
+
+
+

Emerging Solutions

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  • Partial decompression: 5-10x faster
    • +
  • In-place updates: 10-50x improvement
    • +
  • Shared TOAST: 90% WAL reduction
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+
+
+ +
+ +
+

Advanced Optimization Techniques

+ JSONB Optimization Techniques + +
+ +
+

PostgreSQL JSONB vs MongoDB

+ PostgreSQL vs MongoDB Performance + +
+
@@ -30,6 +110,8 @@ // - https://revealjs.com/initialization/ // - https://revealjs.com/config/ Reveal.initialize({ + width: 1920, + height: 1080, hash: true, // Learn about plugins: https://revealjs.com/plugins/ diff --git a/paper/ PGConf NYC 2021 - Understanding of Jsonb Performance.vtt b/paper/ PGConf NYC 2021 - Understanding of Jsonb Performance.vtt new file mode 100644 index 00000000..bed7194c --- /dev/null +++ b/paper/ PGConf NYC 2021 - Understanding of Jsonb Performance.vtt @@ -0,0 +1,2695 @@ +WEBVTT + +1 +00:00:14.469 --> 00:00:17.180 +Thank you very much for attending my lecture. +非常感谢大家听我讲座。 + +2 +00:00:17.180 --> 00:00:21.910 +I'm very happy to be with you. +我很高兴和你在一起。 + +3 +00:00:21.910 --> 00:00:25.320 +And today I will talk about JSONB performance. +今天我将谈谈JSONB的性能。 + +4 +00:00:25.320 --> 00:00:29.090 +This slide are available already. +这张幻灯片已经有售了。 + +5 +00:00:29.090 --> 00:00:33.040 +This joint talk with my colleague Nikita Glukhov. +这是我与同事尼基塔·格卢霍夫的联合讲座。 + +6 +00:00:33.040 --> 00:00:39.470 +This is a picture of an elephant with projects I was working on. +这是一张大象和我正在做的项目的照片。 + +7 +00:00:39.470 --> 00:00:41.199 +Maybe you know some of them. +也许你认识其中一些。 + +8 +00:00:41.199 --> 00:00:53.699 +I'm research scientist at Moscow university and most interesting to me is I'm major Postgres contributor. +我是莫斯科大学的研究科学家，最有趣的是我是Postgres的主要贡献者。 + +9 +00:00:53.699 --> 00:01:00.780 +And my colleague Nikita Glukhov he's working also in Postgres Professional company. +我的同事尼基塔·格卢霍夫也在Postgres Professional公司工作。 + +10 +00:01:00.780 --> 00:01:08.310 +He's also Postgres contributor and for several years he did several big projects. +他也是Postgres的撰稿人，多年来参与了多个大型项目。 + +11 +00:01:08.310 --> 00:01:13.009 +So we today will talk about the JSON performance. +所以今天我们要谈谈JSON的性能。 + +12 +00:01:13.009 --> 00:01:22.400 +And the reason why I decided to talk about this here, we know that JSON is, as we said, +我之所以决定在这里讨论这个，是因为我们知道JSON正如我们所说， + +13 +00:01:22.400 --> 00:01:24.340 +one type fits all. +一种类型适用于所有人。 + +14 +00:01:24.340 --> 00:01:44.370 +You know that modern architecture is micro series architecture and JSON is very good for this architecture because client applications, front end, back end and now database, they use JSON. +你知道现代架构是微系列架构，而JSON非常适合这种架构，因为客户端应用、前端、后端，现在还有数据库，都用JSON。 + +15 +00:01:44.370 --> 00:01:48.630 +It is very, very easy for start up to start their project. +创业公司启动项目非常非常容易。 + +16 +00:01:48.630 --> 00:02:03.909 +You don't need the relational scheme, how to make it proper, how to make it it's very difficult when you start your project, when you start your business to predict what the schema will be. +你不需要关系方案，如何让它变得恰当，当你开始项目、创业时，预测模式会是什么非常困难。 + +17 +00:02:03.909 --> 00:02:05.480 +In the several months. +几个月来， + +18 +00:02:05.480 --> 00:02:07.890 +With JSON you don't have any problem. +用JSON就没问题。 + +19 +00:02:07.890 --> 00:02:10.000 +Just have JSON. +只要有 JSON 就行。 + +20 +00:02:10.000 --> 00:02:23.530 +And all server side languages support JSON SQL JSON so it's not something different from SQL. +而且所有服务器端语言都支持 JSON、SQL、JSON，所以这和 SQL 没什么区别。 + +21 +00:02:23.530 --> 00:02:29.470 +What's very important that JSON relaxed object relational mismatch. +非常重要的是，JSON 放松了对象关系的不匹配。 + +22 +00:02:29.470 --> 00:02:35.660 +In the code you work with object, in relational database you work with relations. +在代码中你处理对象，在关系数据库中你处理关系。 + +23 +00:02:35.660 --> 00:02:41.370 +And you have some contradictions between the programmers and between d/b/a. +而且程序员之间和d/b/a之间存在一些矛盾。 + +24 +00:02:41.370 --> 00:02:47.510 +But when you hear use JSON, there's no any contradiction. +但当你听到使用JSON时，就没有矛盾。 + +25 +00:02:47.510 --> 00:02:52.950 +So this way JSON becomes very, very popular. +这样JSON才变得非常非常受欢迎。 + +26 +00:02:52.950 --> 00:02:57.540 +And I would say that now I see that JSONB rush. +我现在看到了那种 JSONB 的冲刺。 + +27 +00:02:57.540 --> 00:03:12.000 +Because I am speaking in many countries and I see that many people they have they ask me about JSON and they said that we don't know much about SQL. +因为我在很多国家演讲，看到很多人问我关于JSON的事，他们说我们对SQL了解不多。 + +28 +00:03:12.000 --> 00:03:13.670 +We use JSON. + + +29 +00:03:13.670 --> 00:03:17.860 +And what we need is just to have JavaScript instead of SQL. + + +30 +00:03:17.860 --> 00:03:22.000 +This is very interesting career actually. + + +31 +00:03:22.000 --> 00:03:24.370 +I have some thinking about this. + + +32 +00:03:24.370 --> 00:03:28.459 +Because actually it's easy. + + +33 +00:03:28.459 --> 00:03:33.510 +I can internally transform JavaScript to SQL and execute it. + + +34 +00:03:33.510 --> 00:03:38.310 +But that's about my future project. + + +35 +00:03:38.310 --> 00:03:46.650 +And JSONB is actually a main driver of Postgres popularity. + + +36 +00:03:46.650 --> 00:03:50.489 +You see the create table JSON of the it's a common mistake. + + +37 +00:03:50.489 --> 00:03:54.230 +They put everything into JSONB. + + +38 +00:03:54.230 --> 00:04:00.440 +That is because people don't know about how JSONB performance. + + +39 +00:04:00.440 --> 00:04:03.680 +I will talk about this later. + + +40 +00:04:03.680 --> 00:04:14.180 +Another reason about this talk, nobody made the comparison between performance of JSONB operators. + + +41 +00:04:14.180 --> 00:04:22.680 +So this talk I will explain which operator to use, what is better and so on. + + +42 +00:04:22.680 --> 00:04:30.430 +And another reason is that I work 25 years in Postgres, maybe 26. + + +43 +00:04:30.430 --> 00:04:34.440 +I started from 1995. + + +44 +00:04:34.440 --> 00:04:43.100 +And almost all my projects, they connected to the extending Postgres to support this nonstructural data. + + +45 +00:04:43.100 --> 00:05:02.310 +So we started from arrays, h store, full text search, now working on JSONB and SQL and this is my interest and I believe that JSON is very useful for Postgres community. + + +46 +00:05:02.310 --> 00:05:04.610 +You see this picture. + + +47 +00:05:04.610 --> 00:05:14.729 +This how popularity of four databases change over time. + + +48 +00:05:14.729 --> 00:05:18.520 +The only database which grows is Postgres. + + +49 +00:05:18.520 --> 00:05:27.210 +I use the official say official numbers from DB engine and relational popularity. + + +50 +00:05:27.210 --> 00:05:35.900 +Postgres becomes popular from the time we committed JSONB into Postgres. + + +51 +00:05:35.900 --> 00:05:43.720 +I believe that JSONB is one of the main driver of popularity. + + +52 +00:05:43.720 --> 00:06:03.070 +And because No SQL people become upset and go to Postgres because Postgres got good JSON data type. + + +53 +00:06:03.070 --> 00:06:14.009 +Our work on JSONB made possible SQL standard 2016. + + +54 +00:06:14.009 --> 00:06:25.180 +So the success of Postgres made this, all other databases now have JSON and that's why we have SQL standard on this. + + +55 +00:06:25.180 --> 00:06:31.500 +To me it's very important to have to continue work on JSON in Postgres. + + +56 +00:06:31.500 --> 00:06:35.750 +Because we have many, many users. + + +57 +00:06:35.750 --> 00:06:49.480 +These are numbers from PostGreSQL you see that the most popular is JSONB. + + +58 +00:06:49.480 --> 00:06:51.380 +It's the biggest. + + +59 +00:06:51.380 --> 00:07:19.479 +If we take the popularity in the telegram chart on PostGreSQL it's several thousand, any time you have several thousand people on land and JSON and JSONB is third popular award used by Postgres people. + + +60 +00:07:19.479 --> 00:07:23.470 +The first is select, the second is SQL and the third is JSON. + + +61 +00:07:23.470 --> 00:07:29.449 +This is like some argument that JSON is very popular in the Postgres community. + + +62 +00:07:29.449 --> 00:07:38.600 +So we were working on several big projects, some of them already committed, some of them waiting for commit. + + +63 +00:07:38.600 --> 00:07:43.860 +But now we change the priority for our development. + + +64 +00:07:43.860 --> 00:07:49.040 +So we want to have JSONB the first class citizen in Postgres. + + +65 +00:07:49.040 --> 00:08:02.080 +It's means we want to have efficient storage, select, update, good API, the reason for this is SQL JSON is important, of course. + + +66 +00:08:02.080 --> 00:08:04.460 +Because this part of standard. + + +67 +00:08:04.460 --> 00:08:12.729 +But actually people who work with Postgres, they have no idea to be compatible with Oracle or Microsoft. + + +68 +00:08:12.729 --> 00:08:20.330 +I know that people, the startups use started with Postgres and never change it. + + +69 +00:08:20.330 --> 00:08:23.200 +And JSONB is already a mature data type. + + +70 +00:08:23.200 --> 00:08:31.259 +We have a load of functionality in Postgres and we have not enough resources in community. + + +71 +00:08:31.259 --> 00:08:35.940 +To even to review and commit the patches. + + +72 +00:08:35.940 --> 00:08:41.930 +You see that four years we have patches for SQL JSON functions. + + +73 +00:08:41.930 --> 00:08:47.490 +JSON table will wait also four years. + + +74 +00:08:47.490 --> 00:08:49.759 +Maybe for PG15 we'll have some committed. + + +75 +00:08:49.759 --> 00:08:53.370 +But I understand that community just has no resources. + + +76 +00:08:53.370 --> 00:09:02.449 +And my interest mostly is concentrate on improving JSONB, not standard. + + +77 +00:09:02.449 --> 00:09:07.709 +So I mostly aware about Postgres users. + + +78 +00:09:07.709 --> 00:09:18.439 +And I'm not very interested in the compatibility to Oracle or Microsoft SQL server. + + +79 +00:09:18.439 --> 00:09:22.300 +So this is a popular mistake. + + +80 +00:09:22.300 --> 00:09:25.999 +People put everything into JSON and that's not good idea. + + +81 +00:09:25.999 --> 00:09:35.790 +You can see it very easy that ID outside of JSONB and ID inside. + + +82 +00:09:35.790 --> 00:09:43.749 +If JSONB is grow, the performance is great degrade very quickly. + + +83 +00:09:43.749 --> 00:09:50.089 +Don't do this. + + +84 +00:09:50.089 --> 00:09:56.209 +We want to demonstrate the performance of nested containers. + + +85 +00:09:56.209 --> 00:10:04.839 +You usually, we created simple tables with nested objects, and we just test several operators. + + +86 +00:10:04.839 --> 00:10:07.749 +It's error operator. + + +87 +00:10:07.749 --> 00:10:12.329 +Most people use error operator to access the key. + + +88 +00:10:12.329 --> 00:10:17.029 +A hash arrow. + + +89 +00:10:17.029 --> 00:10:19.959 +The new one is subscripting. + + +90 +00:10:19.959 --> 00:10:24.649 +So you can you have like an array syntax. + + +91 +00:10:24.649 --> 00:10:27.079 +Another one the force JSON pass. + + +92 +00:10:27.079 --> 00:10:33.489 +The other one do you know which is better? + + +93 +00:10:33.489 --> 00:10:36.790 +Nobody knows actually and we need to. + + +94 +00:10:36.790 --> 00:10:45.709 +So we did a lot of experiments and now I will show you some graphics. + + +95 +00:10:45.709 --> 00:10:58.009 +This is a raw JSONB size and execution time. + + +96 +00:10:58.009 --> 00:11:00.550 +This is arrow operator. + + +97 +00:11:00.550 --> 00:11:06.360 +JSONB grow and execution times grow. + + +98 +00:11:06.360 --> 00:11:10.139 +So the reason for this I will explain later. + + +99 +00:11:10.139 --> 00:11:13.569 +The same behavior actually is for other operator. + + +100 +00:11:13.569 --> 00:11:18.019 +But it's a bit different. + + +101 +00:11:18.019 --> 00:11:22.149 +Interesting that subscripting behave very good. + + +102 +00:11:22.149 --> 00:11:27.199 +And we have the color is nesting level. + + +103 +00:11:27.199 --> 00:11:34.199 +So we know execution time for the different nesting level. + + +104 +00:11:34.199 --> 00:11:44.980 +This two kilobytes JSONB becomes toasted. + + +105 +00:11:44.980 --> 00:11:50.989 +So we have degradation of performance. + + +106 +00:11:50.989 --> 00:11:57.299 +But before, we have the constant and we have over here for nesting. + + +107 +00:11:57.299 --> 00:12:09.690 +So the deeper we go, the performance is more worse. + + +108 +00:12:09.690 --> 00:12:16.189 +Here is a slowdown, relative to the root level. + + +109 +00:12:16.189 --> 00:12:23.430 +So the picture is also very strange relative to the root level for the same operator. + + +110 +00:12:23.430 --> 00:12:31.269 +So this JSON path and subscription. + + +111 +00:12:31.269 --> 00:12:38.430 +Arrow operator again shows some instability is not very predictable behavior. + + +112 +00:12:38.430 --> 00:12:46.029 +If you see the slowdown relative to the extract pass operator, situation becomes a bit better, + + +113 +00:12:46.029 --> 00:12:53.410 +a bit clearer because we see that JSON path is slowest. + + +114 +00:12:53.410 --> 00:12:56.230 +And subscripting is the fastest. + + +115 +00:12:56.230 --> 00:13:01.999 +Very unexpected behavior, but you know now this. + + +116 +00:13:01.999 --> 00:13:10.959 +After 2 kilobytes, everything becomes more or less because the dominates. + + +117 +00:13:10.959 --> 00:13:19.750 +You have major contribution to the performance. + + +118 +00:13:19.750 --> 00:13:28.230 +This picture demonstrates best operator depending on size and nesting level. + + +119 +00:13:28.230 --> 00:13:33.410 +So it's the same data, but in different picture, different format. + + +120 +00:13:33.410 --> 00:13:41.579 +We see that arrow operator is good only for small JSONB and the root level. + + +121 +00:13:41.579 --> 00:13:47.110 +I would say for root level you can safely use arrow operator. + + +122 +00:13:47.110 --> 00:13:54.939 +For the second, for the first level, I would not use for the big JSONB. + + +123 +00:13:54.939 --> 00:14:00.529 +Subscripting is the most useful good. + + +124 +00:14:00.529 --> 00:14:08.089 +And JSON path, you see here, you see some JSON path. + + +125 +00:14:08.089 --> 00:14:12.230 +But really JSON path is not about performance. + + +126 +00:14:12.230 --> 00:14:19.970 +Because JSON path is very flexible for the very complex queries you need JSON path. + + +127 +00:14:19.970 --> 00:14:31.019 +But for the simple queries like this, over here the JSON path is too big. + + +128 +00:14:31.019 --> 00:14:37.470 +So all operators have common overhead, it's deTOAST and iteration time. + + +129 +00:14:37.470 --> 00:14:54.470 +But arrow operator very fast for the small and root level because it has minimal initialization but it need to copy intermediate results to some temporary datums. + + +130 +00:14:54.470 --> 00:14:56.209 +Let's see this one. + + +131 +00:14:56.209 --> 00:15:02.029 +This picture how arrow operator executes. + + +132 +00:15:02.029 --> 00:15:11.239 +So when you have TOAST, this is TOAST pointer, then you find key 1. + + +133 +00:15:11.239 --> 00:15:23.959 +You find some value and you copy all this nested JSONB container into some intermediate data which go to the second execution. + + +134 +00:15:23.959 --> 00:15:29.439 +So key 2 and then you copy string to result. + + +135 +00:15:29.439 --> 00:15:34.899 +This example just for root in the first level. + + +136 +00:15:34.899 --> 00:15:42.509 +But you can imagine if you have 9 levels, big more levels, you have to repeat this operation. + + +137 +00:15:42.509 --> 00:15:52.019 +You have to copy all nested container to memory, to some intermediate datum. + + +138 +00:15:52.019 --> 00:15:57.929 +This surprise for the abstraction. + + +139 +00:15:57.929 --> 00:16:00.920 +In Postgres you can combine operator. + + +140 +00:16:00.920 --> 00:16:05.609 +So this way this arrow operator works like this. + + +141 +00:16:05.609 --> 00:16:09.279 +But extract path works different. + + +142 +00:16:09.279 --> 00:16:18.279 +JSON path, they have they share the same schema. + + +143 +00:16:18.279 --> 00:16:20.609 +Again, you have TOAST pointer. + + +144 +00:16:20.609 --> 00:16:24.699 +You deTOAST and then you just copy string to result. + + +145 +00:16:24.699 --> 00:16:34.790 +You find everything in one insight and there is no inside and there is no copy over here. + + +146 +00:16:34.790 --> 00:16:44.339 +So the conclusion is that you can use safely arrow operator for root level of any size, + + +147 +00:16:44.339 --> 00:16:46.979 +and for first level for small JSONB. + + +148 +00:16:46.979 --> 00:16:55.869 +Then use subscripting and extract path for large JSONB and if you have higher nesting level. + + +149 +00:16:55.869 --> 00:16:56.869 +This is my recommendation. + + +150 +00:16:56.869 --> 00:17:03.359 +JSON path is slowest, but it's very useful for complex queries. + + +151 +00:17:03.359 --> 00:17:15.209 +Now we want to analyze the performance of another very important queries is contains. + + +152 +00:17:15.209 --> 00:17:20.939 +So you want to find some key value in JSONB. + + +153 +00:17:20.939 --> 00:17:27.390 +So again, we have a table with arrays of various size. + + +154 +00:17:27.390 --> 00:17:32.799 +From 1 to 1 million entries. + + +155 +00:17:32.799 --> 00:17:39.779 +And we try to find several operations. + + +156 +00:17:39.779 --> 00:17:42.139 +It's contains operator. + + +157 +00:17:42.139 --> 00:17:45.880 +JSON pass match operator. + + +158 +00:17:45.880 --> 00:17:51.490 +JSON path exist operator with filter. + + +159 +00:17:51.490 --> 00:17:55.029 +And SQL, two variants of SQL. + + +160 +00:17:55.029 --> 00:18:01.529 +One is exists and another one is optimized version of this one. + + +161 +00:18:01.529 --> 00:18:03.049 +And we use this query. + + +162 +00:18:03.049 --> 00:18:09.160 +Like we use we look for the first element existed. + + +163 +00:18:09.160 --> 00:18:10.380 +Actually it's a zero. + + +164 +00:18:10.380 --> 00:18:15.059 +And we look for the nonexistent operators which is minus 1. + + +165 +00:18:15.059 --> 00:18:20.179 +And we see how different queries execute. + + +166 +00:18:20.179 --> 00:18:30.750 +We see that if we apply search first element the behavior more or less the same, except two. + + +167 +00:18:30.750 --> 00:18:34.230 +Contain separator is the fastest. + + +168 +00:18:34.230 --> 00:18:43.639 +Before TOAST, before JSONB TOASTed we have constant and constant time. + + +169 +00:18:43.639 --> 00:18:50.390 +And also we have much operator in lux mode. + + +170 +00:18:50.390 --> 00:19:09.450 +JSON path has two modes, which you instruct interpreter of JSON path how to work with errors for example. + + +171 +00:19:09.450 --> 00:19:15.190 +In lax mode. + + +172 +00:19:15.190 --> 00:19:19.880 +Execution stops when you find the result. + + +173 +00:19:19.880 --> 00:19:27.929 +And since zero is the first member in the array, it happens very fast. + + +174 +00:19:27.929 --> 00:19:37.450 +But in strict mode, green, you have to check all elements. + + +175 +00:19:37.450 --> 00:19:44.950 +Because in strict mode you have to see all errors, possible errors. + + +176 +00:19:44.950 --> 00:19:47.919 +Fortunately lax mode is default. + + +177 +00:19:47.919 --> 00:20:04.110 +So you see that search first element but for nonexistent element, behavior the same because you have to check all elements. + + +178 +00:20:04.110 --> 00:20:08.289 +You check all elements, you can find minus zero. + + +179 +00:20:08.289 --> 00:20:14.899 +And the difference in performance is just different overheads. + + +180 +00:20:14.899 --> 00:20:16.639 +Operator. + + +181 +00:20:16.639 --> 00:20:20.940 +So this is speed up relative to SQL exists. + + +182 +00:20:20.940 --> 00:20:26.200 +And conclusions is that contains is the fastest. + + +183 +00:20:26.200 --> 00:20:30.559 +SQL exists is the slowest. + + +184 +00:20:30.559 --> 00:20:47.059 +And the performance between much and exist operators for JSON path depends how many items you have to iterate. + + +185 +00:20:47.059 --> 00:20:57.220 +The most interesting, you see on all pictures, you see that performance very dependent on how big is JSON. + + +186 +00:20:57.220 --> 00:21:04.399 +After JSON in 2 kilobytes, the performance degrade. + + +187 +00:21:04.399 --> 00:21:08.930 +That's why we analyze the TOAST details. + + +188 +00:21:08.930 --> 00:21:13.470 +This part I call the curse of TOAST. + + +189 +00:21:13.470 --> 00:21:16.330 +This is unpredictable performance of JSONB. + + +190 +00:21:16.330 --> 00:21:22.909 +People actually ask me why when they start the project we have very good performance, + + +191 +00:21:22.909 --> 00:21:28.600 +but then we see sometimes that performance very unstable. + + +192 +00:21:28.600 --> 00:21:34.129 +And this query, this example demonstrates unpredictable behavior. + + +193 +00:21:34.129 --> 00:21:40.330 +So very simple JSON with ID and some array. + + +194 +00:21:40.330 --> 00:21:47.899 +And first you have select very fast. + + +195 +00:21:47.899 --> 00:21:56.129 +So we see that buffers hit 2,500 and some milliseconds. + + +196 +00:21:56.129 --> 00:21:59.500 +After you update, very simple update. + + +197 +00:21:59.500 --> 00:22:03.700 +You have 30,000 buffers. + + +198 +00:22:03.700 --> 00:22:07.080 +And 6 milliseconds. + + +199 +00:22:07.080 --> 00:22:18.880 +So people asking me why it happens, but it happens because rows gets TOASTed. + + +200 +00:22:18.880 --> 00:22:30.009 +So TOAST is a very useful technology in Postgres which allows you to store very long objects. + + +201 +00:22:30.009 --> 00:22:34.889 +We have limitation for 8 kilobyte page size. + + +202 +00:22:34.889 --> 00:22:38.389 +We have tuple limit to 2 kilobytes. + + +203 +00:22:38.389 --> 00:22:42.679 +Everything that's bigger to 2 kilobytes we move to storage. + + +204 +00:22:42.679 --> 00:22:50.460 +And we have implicit join when we get value. + + +205 +00:22:50.460 --> 00:22:53.520 +But situation is much worse, I will show later. + + +206 +00:22:53.520 --> 00:22:55.259 +But here is explanation. + + +207 +00:22:55.259 --> 00:23:12.129 +You can install very useful extension page inspect and see that regional JSON, which stored in line, not TOASTed in heap, we have 2,500 pages. + + +208 +00:23:12.129 --> 00:23:16.169 +And just 4tuples per page. + + +209 +00:23:16.169 --> 00:23:27.659 +After update, we see very strange that we have now 64 pages with 157 tuples per page. + + +210 +00:23:27.659 --> 00:23:28.659 +How this happens? + + +211 +00:23:28.659 --> 00:23:35.750 +Because with long JSON replaced by just tuple TOAST pointer. + + +212 +00:23:35.750 --> 00:23:40.029 +So tuple becomes very small. + + +213 +00:23:40.029 --> 00:23:43.840 +But number but everything move to the TOAST. + + +214 +00:23:43.840 --> 00:23:54.670 +And we can find using this query, we can find the name of TOAST relation very easy. + + +215 +00:23:54.670 --> 00:24:05.179 +And then we can inspect how about chunks where we store this long query in the TOAST. + + +216 +00:24:05.179 --> 00:24:10.340 +And access to the TOAST requires reading at least 3 additional buffers. + + +217 +00:24:10.340 --> 00:24:19.220 +Two TOAST index buffers, because you access TOAST not directly, but using the B tree index. + + +218 +00:24:19.220 --> 00:24:25.450 +So you access 2 TOAST index buffers and one TOAST from heap buffer. + + +219 +00:24:25.450 --> 00:24:33.240 +So easy calculation you explain that why we have 30,000 pages after update. + + +220 +00:24:33.240 --> 00:24:41.390 +We have 64 buffers, and overhead 3 buffers multiplied by number of rows. + + +221 +00:24:41.390 --> 00:24:42.700 +10,000. + + +222 +00:24:42.700 --> 00:24:49.120 +So this explains performance of the small update. + + +223 +00:24:49.120 --> 00:24:52.250 +And if you know what is TOAST. + + +224 +00:24:52.250 --> 00:24:56.309 +I can explain it's very detailed. + + +225 +00:24:56.309 --> 00:25:06.639 +TOAST compressed and then split into 2 kilobyte chunks and stored in the normal heap relations. + + +226 +00:25:06.639 --> 00:25:08.220 +You just don't see. + + +227 +00:25:08.220 --> 00:25:10.830 +It's hidden from you. + + +228 +00:25:10.830 --> 00:25:14.299 +And how to access these chunks. + + +229 +00:25:14.299 --> 00:25:17.320 +You have to use index. + + +230 +00:25:17.320 --> 00:25:23.470 +Over here only bytes from the first chunk. + + +231 +00:25:23.470 --> 00:25:30.269 +You hid 3, 4, 5 or additional blocks. + + +232 +00:25:30.269 --> 00:25:32.650 +That's the problem of TOAST. + + +233 +00:25:32.650 --> 00:25:37.759 +And TOAST also very complicated algorithm. + + +234 +00:25:37.759 --> 00:25:40.360 +It use four passes. + + +235 +00:25:40.360 --> 00:25:47.500 +So Postgres try to compact tuple to 2 kilobytes. + + +236 +00:25:47.500 --> 00:25:52.320 +We tried to compress the longest fields. + + +237 +00:25:52.320 --> 00:26:06.820 +If it's not compressed, if the result of tuple still more than 2 kilobytes, it replace fields by TOAST pointer and fields move to the TOAST relation. + + +238 +00:26:06.820 --> 00:26:12.029 +Actually you see that pass 1, pass 2, pass 3, pass 4. + + +239 +00:26:12.029 --> 00:26:13.600 +So it's not easy. + + +240 +00:26:13.600 --> 00:26:18.679 +The original tuple replaced by this. + + +241 +00:26:18.679 --> 00:26:24.529 +This is plain, some field which not touched. + + +242 +00:26:24.529 --> 00:26:33.639 +This is compressed field and 4 tuple pointers which pointed to the TOAST storage. + + +243 +00:26:33.639 --> 00:26:35.820 +We have here. + + +244 +00:26:35.820 --> 00:26:44.149 +And when you access, for example, this one, you have to read all this one. + + +245 +00:26:44.149 --> 00:26:50.840 +Even if you access plain attribute, you don't touch all this TOAST. + + +246 +00:26:50.840 --> 00:27:01.879 +But once the attribute is TOASTed, you have to combine all these chunks. + + +247 +00:27:01.879 --> 00:27:10.990 +First you need to find all chunks, combine to one buffer. + + +248 +00:27:10.990 --> 00:27:12.820 +And then decompress. + + +249 +00:27:12.820 --> 00:27:15.529 +So a lot of overhead. + + +250 +00:27:15.529 --> 00:27:18.809 +Let's see this example. + + +251 +00:27:18.809 --> 00:27:20.139 +Example is very easy. + + +252 +00:27:20.139 --> 00:27:31.110 +We have hundred JSONB of different sizes and JSONB looks like key 1 very long key 2 array, + + +253 +00:27:31.110 --> 00:27:34.379 +key 3 and key 4. + + +254 +00:27:34.379 --> 00:27:38.749 +We measure time of arrow operator. + + +255 +00:27:38.749 --> 00:27:43.190 +We actually we repeat 1,000 times in query. + + +256 +00:27:43.190 --> 00:27:46.710 +Then to have a more stable result. + + +257 +00:27:46.710 --> 00:27:48.169 +This result. + + +258 +00:27:48.169 --> 00:27:49.629 +You see? + + +259 +00:27:49.629 --> 00:27:53.000 +So we generate the query. + + +260 +00:27:53.000 --> 00:28:01.270 +We execute 1,000, and then result the time, divide by 1,000, to have more stable time. + + +261 +00:28:01.270 --> 00:28:08.750 +And we see the key access time literally increase JSONB size. + + +262 +00:28:08.750 --> 00:28:12.910 +Regardless of size and position. + + +263 +00:28:12.910 --> 00:28:19.119 +And this is very surprise for many people because they say I have just one small key. + + +264 +00:28:19.119 --> 00:28:23.610 +Why I my access time is so big? + + +265 +00:28:23.610 --> 00:28:31.629 +Because everything after here after the hundred kilobytes, everything in TOAST. + + +266 +00:28:31.629 --> 00:28:38.649 +And to get this small key, you have to deTOAST all this JSONB. + + +267 +00:28:38.649 --> 00:28:41.220 +And then decompress. + + +268 +00:28:41.220 --> 00:28:43.919 +You see three areas. + + +269 +00:28:43.919 --> 00:28:46.920 +One is in line. + + +270 +00:28:46.920 --> 00:28:51.009 +The performance is good, and time is constant. + + +271 +00:28:51.009 --> 00:28:53.940 +The second one is expressed in line. + + +272 +00:28:53.940 --> 00:29:00.980 +When Postgres success to compress and put into inline. + + +273 +00:29:00.980 --> 00:29:06.360 +So hundred kilobytes is actually 2 kilobytes compressed. + + +274 +00:29:06.360 --> 00:29:10.019 +Because here's a row. + + +275 +00:29:10.019 --> 00:29:14.149 +After compressed 100 kilobyte becomes 2 kilobyte. + + +276 +00:29:14.149 --> 00:29:21.279 +And you have some growth access time because you have to decompress. + + +277 +00:29:21.279 --> 00:29:30.340 +And after 100 kilobytes, everything toasted. + + +278 +00:29:30.340 --> 00:29:33.350 +Here is a number of blocks. + + +279 +00:29:33.350 --> 00:29:43.230 +200 kilobytes, you see no additional block to read because everything in line. + + +280 +00:29:43.230 --> 00:29:47.549 +After you read more, more, more, more blocks, you see 30 blocks, it's too much. + + +281 +00:29:47.549 --> 00:29:56.779 +>> Obviously these blocks are it might be worrying [off microphone] have you done a comparison to Mongo I'll ask the question again. + + +282 +00:29:56.779 --> 00:30:04.049 +Have you done a comparison with Mongo and in what areas does Mongo suffer these same issues? + + +283 +00:30:04.049 --> 00:30:11.440 +>> OLEG BARTUNOV: The last slide will be about some comparison with Mongo because that's all people is interested. + + +284 +00:30:11.440 --> 00:30:12.590 +Yes. + + +285 +00:30:12.590 --> 00:30:17.190 +This is the same. + + +286 +00:30:17.190 --> 00:30:20.470 +Now we see this is compressed size. + + +287 +00:30:20.470 --> 00:30:24.820 +So we see that only two areas. + + +288 +00:30:24.820 --> 00:30:26.860 +Inline this is size 2 kilobytes. + + +289 +00:30:26.860 --> 00:30:30.100 +After 2 kilobytes it would compress in line. + + +290 +00:30:30.100 --> 00:30:38.720 +In line they are more clearly seen because of their size is compressed 2 kilobytes. + + +291 +00:30:38.720 --> 00:30:48.740 +And the problem is access time doesn't depends on the key size and position. + + +292 +00:30:48.740 --> 00:30:53.980 +Everything suffers from the TOAST. + + +293 +00:30:53.980 --> 00:30:56.610 +Another problem is partial update. + + +294 +00:30:56.610 --> 00:31:02.110 +Because people also complain that I just want to update small key. + + +295 +00:31:02.110 --> 00:31:04.460 +Why the performance very bad? + + +296 +00:31:04.460 --> 00:31:16.970 +Again, again, because Postgres TOAST, because TOAST mechanism, algorithm works with JSONB as a black box. + + +297 +00:31:16.970 --> 00:31:27.230 +Because it was developed by when all data types were atomic. + + +298 +00:31:27.230 --> 00:31:32.740 +But now JSONB has a structure as other data type. + + +299 +00:31:32.740 --> 00:31:36.100 +And TOAST should be more smart. + + +300 +00:31:36.100 --> 00:31:44.149 +But currently TOAST storage duplicated when we update, well traffic increased and performance becomes very slow. + + +301 +00:31:44.149 --> 00:31:47.960 +Also you see the example. + + +302 +00:31:47.960 --> 00:31:57.940 +We have hundred gigabytes heap and relation 7. + + +303 +00:31:57.940 --> 00:32:15.409 +After update we have TOAST table doubled but also we have 130 megabytes well traffic. + + +304 +00:32:15.409 --> 00:32:17.720 +After small update. + + +305 +00:32:17.720 --> 00:32:22.999 +Because Postgres doesn't know anything about structure of JSONB. + + +306 +00:32:22.999 --> 00:32:31.039 +It just black box, double size and this is problem. + + +307 +00:32:31.039 --> 00:32:37.519 +So we have our project started this year in the beginning of this year. + + +308 +00:32:37.519 --> 00:32:42.620 +JSONB deTOAST improvement and our goal, ideal goal. + + +309 +00:32:42.620 --> 00:32:48.129 +So we won't have new dependency on JSONB size and position. + + +310 +00:32:48.129 --> 00:32:57.320 +Our access time should be proportional to the nesting level and update time should be proportional to nesting level. + + +311 +00:32:57.320 --> 00:32:59.870 +And the key size what we update. + + +312 +00:32:59.870 --> 00:33:04.809 +Not the whole JSONB size but what we update only. + + +313 +00:33:04.809 --> 00:33:08.379 +Original TOAST doesn't use inline. + + +314 +00:33:08.379 --> 00:33:16.649 +Once you TOAST, we have a lot of space in heap just free. + + +315 +00:33:16.649 --> 00:33:26.480 +So we want to utilize inline as much as possible because access to inline many times faster than to the toast. + + +316 +00:33:26.480 --> 00:33:33.889 +And we want to compress long fields in TOAST chunks separately for independent access and update. + + +317 +00:33:33.889 --> 00:33:38.640 +So if you want to update something, you don't need to touch all chunks. + + +318 +00:33:38.640 --> 00:33:44.039 +You need to find some chunk updated and log. + + +319 +00:33:44.039 --> 00:33:45.039 +That's all. + + +320 +00:33:45.039 --> 00:33:47.399 +But this ideal. + + +321 +00:33:47.399 --> 00:33:51.399 +And we have several experiments. + + +322 +00:33:51.399 --> 00:33:54.090 +So woo have a partial decompression. + + +323 +00:33:54.090 --> 00:33:57.429 +We saw JSONB object key by length. + + +324 +00:33:57.429 --> 00:34:04.159 +So short keys, they stored in the beginning. + + +325 +00:34:04.159 --> 00:34:12.700 +Partial deTOAST, partial decompression, in lapse line toast, compressed fields TOAST not much time. + + +326 +00:34:12.700 --> 00:34:15.660 +So I just say their names. + + +327 +00:34:15.660 --> 00:34:18.830 +But inplace updates. + + +328 +00:34:18.830 --> 00:34:22.030 +And here we see the results. + + +329 +00:34:22.030 --> 00:34:24.770 +This is a master. + + +330 +00:34:24.770 --> 00:34:27.310 +How master behave. + + +331 +00:34:27.310 --> 00:34:40.179 +After, for example, after partial depression, after the partial decompression, some keys becomes faster. + + +332 +00:34:40.179 --> 00:34:43.639 +Here is all keys behave the same. + + +333 +00:34:43.639 --> 00:34:52.059 +But after partial decompression some keys become faster because they're in the beginning and decompressed faster. + + +334 +00:34:52.059 --> 00:34:56.230 +After the sorting keys, we see another keys came down. + + +335 +00:34:56.230 --> 00:35:03.490 +Because key 3 for example was blocked by long key 2. + + +336 +00:35:03.490 --> 00:35:09.810 +Was blocked after the sorting key 3 becomes behind the long objects. + + +337 +00:35:09.810 --> 00:35:15.099 +And this is, you see, it becomes lower. + + +338 +00:35:15.099 --> 00:35:22.480 +And after all this experiment, we get very interesting results, very good result. + + +339 +00:35:22.480 --> 00:35:29.470 +So we have still growing time but this we understand for the big arrays. + + +340 +00:35:29.470 --> 00:35:31.710 +Big arrays. + + +341 +00:35:31.710 --> 00:35:39.280 +And the first element in array we access faster than the last, of course. + + +342 +00:35:39.280 --> 00:35:41.619 +But what to do with this? + + +343 +00:35:41.619 --> 00:35:44.799 +We find it's another problem. + + +344 +00:35:44.799 --> 00:35:59.890 +But you see some very simple optimization with still stay with heap with TOAST, we can find like several orders of magnitude performance gain. + + +345 +00:35:59.890 --> 00:36:10.080 +Here is a very interesting picture how much different optimization gain to the performance. + + +346 +00:36:10.080 --> 00:36:13.380 +So we see that this is the short keys. + + +347 +00:36:13.380 --> 00:36:17.200 +Key 1 and key 3 for example here. + + +348 +00:36:17.200 --> 00:36:20.849 +Key 3 green sorting. + + +349 +00:36:20.849 --> 00:36:28.270 +Because it was blocked by key 2 but after sorting keys it got a lot of performance gain. + + +350 +00:36:28.270 --> 00:36:29.270 +And so on. + + +351 +00:36:29.270 --> 00:36:34.280 +So it's easy to interpret this picture. + + +352 +00:36:34.280 --> 00:36:37.369 +Slides available but not much time. + + +353 +00:36:37.369 --> 00:36:45.810 +And if we return back to this popular mistakes, the mistakes becomes not very serious. + + +354 +00:36:45.810 --> 00:37:00.450 +Because now this ID, which stored inside JSONB, not growing infinitely, but stay constant, still have some overhead, but it's okay, more or less. + + +355 +00:37:00.450 --> 00:37:03.930 +After all this experiment. + + +356 +00:37:03.930 --> 00:37:08.609 +We have also make some experimental sliced deTOAST. + + +357 +00:37:08.609 --> 00:37:13.950 +To improve access to array element stored in chance. + + +358 +00:37:13.950 --> 00:37:30.830 +And the last you see this arrays elements, they now not growing infinitely but this is very experimental. + + +359 +00:37:30.830 --> 00:37:31.830 +Update. + + +360 +00:37:31.830 --> 00:37:40.109 +Update is very, very painful process in Postgres. + + +361 +00:37:40.109 --> 00:37:43.780 +And for JSONB especially. + + +362 +00:37:43.780 --> 00:37:47.790 +Here is a master. + + +363 +00:37:47.790 --> 00:37:56.339 +After the shared TOAST, shared TOAST means we update only selected chunks. + + +364 +00:37:56.339 --> 00:38:01.710 +Other chunks were shared. + + +365 +00:38:01.710 --> 00:38:17.650 +After the shared TOAST we have good results and only on the last array elements still grow because the last element in the array. + + +366 +00:38:17.650 --> 00:38:32.119 +In in place update, we have I think it's good, not good results for updates. + + +367 +00:38:32.119 --> 00:38:37.049 +Again for updates we have again several orders of magnitude. + + +368 +00:38:37.049 --> 00:38:45.790 +Updates is very important, because people use JSONB in OETP environment. + + +369 +00:38:45.790 --> 00:38:48.559 +So update is very important. + + +370 +00:38:48.559 --> 00:38:52.180 +Access is good for analytical processing. + + +371 +00:38:52.180 --> 00:38:58.710 +But for LTP update is very important. + + +372 +00:38:58.710 --> 00:39:01.319 +This is a number of blocks read. + + +373 +00:39:01.319 --> 00:39:09.069 +You see clearly we have much less blocks to read. + + +374 +00:39:09.069 --> 00:39:16.470 +Just to remind you, this is not linear scale, this is algorithmic scale. + + +375 +00:39:16.470 --> 00:39:19.790 +This is WAL traffic. + + +376 +00:39:19.790 --> 00:39:23.370 +In master you have very, very big WAL traffic. + + +377 +00:39:23.370 --> 00:39:26.540 +In shared it's smaller. + + +378 +00:39:26.540 --> 00:39:30.740 +And here we have very controlled WAL traffic. + + +379 +00:39:30.740 --> 00:39:38.170 +You looked only what you update. + + +380 +00:39:38.170 --> 00:39:49.740 +The remain people asking what if I use JSONB and relational structure, which is better? + + +381 +00:39:49.740 --> 00:39:57.060 +So we made several again access the whole document testing. + + +382 +00:39:57.060 --> 00:40:13.640 +So this JSONB, this relational tables for relational, you need to have join, for JSON you don't need join and we have several queries and this is the result picture. + + +383 +00:40:13.640 --> 00:40:25.900 +So you see that this is a very, very fast the darker blue is good. + + +384 +00:40:25.900 --> 00:40:27.599 +This is bad. + + +385 +00:40:27.599 --> 00:40:34.530 +To access the whole document in JSONB it's no surprise, very fast. + + +386 +00:40:34.530 --> 00:40:37.869 +Because you don't need to just need to read it. + + +387 +00:40:37.869 --> 00:40:49.200 +And for but when you want to transfer JSONB, you have problem. + + +388 +00:40:49.200 --> 00:40:53.730 +Because you need to convert JSONB to the text. + + +389 +00:40:53.730 --> 00:40:57.070 +Conversion to the text in Postgres is also painful. + + +390 +00:40:57.070 --> 00:41:21.420 +You need to check all bits, you know, to characters so it's not easy operation and we see for the large JSONB the time is not very good and it becomes also here. + + +391 +00:41:21.420 --> 00:41:30.740 +Here we made experiment when we don't need to transfer in textual forum as text. + + +392 +00:41:30.740 --> 00:41:37.960 +This is called UB JSON we transfer to the client just binary transfer. + + +393 +00:41:37.960 --> 00:41:40.640 +We see it becomes better. + + +394 +00:41:40.640 --> 00:41:45.880 +There is no degradation. + + +395 +00:41:45.880 --> 00:41:52.721 +For the relational, you see the problem. + + +396 +00:41:52.721 --> 00:41:58.339 +We transfer this is the time for select. + + +397 +00:41:58.339 --> 00:42:03.670 +This is for select transfer and this is transfer binary. + + +398 +00:42:03.670 --> 00:42:07.339 +There is a method, for arrays you can transfer binary. + + +399 +00:42:07.339 --> 00:42:14.290 +And clearly we see that JSONB here is the winner. + + +400 +00:42:14.290 --> 00:42:18.760 +And this explain why it's so popular. + + +401 +00:42:18.760 --> 00:42:21.510 +Because micro service, what is a micro service? + + +402 +00:42:21.510 --> 00:42:29.720 +It's a small service which expects some predefined query, some aggregate. + + +403 +00:42:29.720 --> 00:42:34.020 +And JSONB is a actually aggregate. + + +404 +00:42:34.020 --> 00:42:42.120 +You don't need to join data from different tables. + + +405 +00:42:42.120 --> 00:42:43.920 +You just have aggregation. + + +406 +00:42:43.920 --> 00:42:48.770 +And microservice access this JSONB and performance is very good. + + +407 +00:42:48.770 --> 00:42:50.599 +Very simple. + + +408 +00:42:50.599 --> 00:42:56.980 +So if you use microservice architecture you'll just happy with JSONB. + + +409 +00:42:56.980 --> 00:42:58.320 +That's popular. + + +410 +00:42:58.320 --> 00:43:06.530 +But when you access key and update, you have a bit different result. + + +411 +00:43:06.530 --> 00:43:15.410 +Because the first one is the last one is select. + + +412 +00:43:15.410 --> 00:43:27.849 +The relational, current situation, and this situation with JSONB after all our optimization. + + +413 +00:43:27.849 --> 00:43:33.500 +So you see that after all optimization, as fast as relational access. + + +414 +00:43:33.500 --> 00:43:41.940 +We understand that for JSONB to get some key, you have to do some operation. + + +415 +00:43:41.940 --> 00:43:47.760 +But for relational, you just need to get some you don't need any overhead. + + +416 +00:43:47.760 --> 00:43:57.550 +And very nice is that after our optimization, we behave the same as relational. + + +417 +00:43:57.550 --> 00:44:01.670 +But for current situation like this. + + +418 +00:44:01.670 --> 00:44:08.020 +So if you want to update especially, update access key, relational is the winner. + + +419 +00:44:08.020 --> 00:44:12.619 +Our optimization helps a lot. + + +420 +00:44:12.619 --> 00:44:15.470 +This is slowdown. + + +421 +00:44:15.470 --> 00:44:23.230 +You see that JSONB slow down against this relational. + + +422 +00:44:23.230 --> 00:44:28.220 +But after our optimization, we have you see like the same. + + +423 +00:44:28.220 --> 00:44:31.799 +Like relational. + + +424 +00:44:31.799 --> 00:44:34.650 +And the same is update slowdown. + + +425 +00:44:34.650 --> 00:44:46.770 +So for update we still have some this is JSONB original in master, and here is our. + + +426 +00:44:46.770 --> 00:44:51.359 +So our optimization helps for updates. + + +427 +00:44:51.359 --> 00:44:54.880 +And also WAL traffic. + + +428 +00:44:54.880 --> 00:45:00.809 +So we're here for master, we hit a lot of WAL traffic. + + +429 +00:45:00.809 --> 00:45:02.609 +We look for a lot. + + +430 +00:45:02.609 --> 00:45:08.500 +This is relational and this is our optimization. + + +431 +00:45:08.500 --> 00:45:12.670 +There looks like the same. + + +432 +00:45:12.670 --> 00:45:16.110 +Also we have access array member. + + +433 +00:45:16.110 --> 00:45:17.300 +Very popular operation. + + +434 +00:45:17.300 --> 00:45:21.530 +We have array and you want to get some member of this array. + + +435 +00:45:21.530 --> 00:45:32.099 +We have first key, middle key and the last key. + + +436 +00:45:32.099 --> 00:45:39.680 +And here's relational, optimized, and non optimized JSONB. + + +437 +00:45:39.680 --> 00:45:50.000 +So we see access array member is not very good for JSONB, but with our optimization, + + +438 +00:45:50.000 --> 00:45:56.059 +again, again we approach to the relational. + + +439 +00:45:56.059 --> 00:46:02.650 +And update array member, we also compare how to update array member. + + +440 +00:46:02.650 --> 00:46:05.960 +And here is JSONB. + + +441 +00:46:05.960 --> 00:46:09.920 +This is JSONB optimize and this is relational. + + +442 +00:46:09.920 --> 00:46:15.869 +Of course when you update array member in relational table, it's very easy. + + +443 +00:46:15.869 --> 00:46:21.020 +You just update one row. + + +444 +00:46:21.020 --> 00:46:27.539 +And for the JSONB, you update the whole JSONB. + + +445 +00:46:27.539 --> 00:46:30.839 +We can be very big. + + +446 +00:46:30.839 --> 00:46:37.670 +But our optimization helps a lot again. + + +447 +00:46:37.670 --> 00:46:39.420 +This is a WAL. + + +448 +00:46:39.420 --> 00:46:46.109 +And conclusion is that JSONB is good for full object access. + + +449 +00:46:46.109 --> 00:46:47.329 +So microservices. + + +450 +00:46:47.329 --> 00:46:49.190 +It's much faster than relational way. + + +451 +00:46:49.190 --> 00:46:55.480 +In relational way you have to join, have you to aggregate and very difficult to tune the process. + + +452 +00:46:55.480 --> 00:46:57.900 +With JSONB you have no problem. + + +453 +00:46:57.900 --> 00:47:00.799 +Also JSONB is very good for storing metadata. + + +454 +00:47:00.799 --> 00:47:07.710 +In short, metadata in separate JSONB field. + + +455 +00:47:07.710 --> 00:47:14.289 +And currently PG14 not optimized, as I showed you, for update. + + +456 +00:47:14.289 --> 00:47:16.430 +And access to array member. + + +457 +00:47:16.430 --> 00:47:27.400 +But we demonstrated all our optimization, which give which resulted in orders of magnitudes for select and update. + + +458 +00:47:27.400 --> 00:47:35.270 +And the question is how to integrate now all this, our patches, to the Postgres. + + +459 +00:47:35.270 --> 00:47:38.920 +And the first step is to make a data type aware TOAST. + + +460 +00:47:38.920 --> 00:47:45.470 +Because currently TOAST is the common for all data type. + + +461 +00:47:45.470 --> 00:47:52.330 +But we suggest that TOAST should be extended. + + +462 +00:47:52.330 --> 00:47:57.990 +So data type knows better how to TOAST it. + + +463 +00:47:57.990 --> 00:48:10.819 +And that allows us to, allows many other developers give a lot of performance improving. + + +464 +00:48:10.819 --> 00:48:15.900 +We have example when we improve streaming. + + +465 +00:48:15.900 --> 00:48:23.880 +You know, some people want to stream data into the Postgres. + + +466 +00:48:23.880 --> 00:48:25.460 +For example, movie. + + +467 +00:48:25.460 --> 00:48:29.410 +It's crazy, but they stream there. + + +468 +00:48:29.410 --> 00:48:34.970 +Before you image how it behaves slowly because it's logged every time. + + +469 +00:48:34.970 --> 00:48:43.540 +You add one byte and you look all one gigabyte. + + +470 +00:48:43.540 --> 00:48:49.930 +After our optimization we have special extension, it works very fast. + + +471 +00:48:49.930 --> 00:48:52.180 +We look only this one byte. + + +472 +00:48:52.180 --> 00:48:54.650 +That's all. + + +473 +00:48:54.650 --> 00:49:01.810 +Because we made a special for byte here, special TOAST. + + +474 +00:49:01.810 --> 00:49:09.650 +So we need to the community accept some data type. + + +475 +00:49:09.650 --> 00:49:11.710 +Just two slides. + + +476 +00:49:11.710 --> 00:49:14.309 +So we have to do. + + +477 +00:49:14.309 --> 00:49:20.849 +On physical level we provide random access to keys and arrays. + + +478 +00:49:20.849 --> 00:49:22.809 +On physical level this is easy. + + +479 +00:49:22.809 --> 00:49:26.720 +We already have sliced deTOAST. + + +480 +00:49:26.720 --> 00:49:27.880 +We need to do some compression. + + +481 +00:49:27.880 --> 00:49:32.220 +But it's most important to make it at the logical level. + + +482 +00:49:32.220 --> 00:49:40.170 +So we know how to which toast chink we need to work on logical level. + + +483 +00:49:40.170 --> 00:49:43.150 +And this number of patches. + + +484 +00:49:43.150 --> 00:49:47.359 +This not exists, not exists yes. + + +485 +00:49:47.359 --> 00:49:57.490 +But this all our patches and our roadmap how to work with community to submit all this picture. + + +486 +00:49:57.490 --> 00:50:00.579 +All this our result. + + +487 +00:50:00.579 --> 00:50:03.049 +And references. + + +488 +00:50:03.049 --> 00:50:06.880 +And I invited you to join our development team. + + +489 +00:50:06.880 --> 00:50:12.650 +This is not a company project, this is open source community project. + + +490 +00:50:12.650 --> 00:50:14.480 +So everybody invited to join us. + + +491 +00:50:14.480 --> 00:50:23.950 +This is what asked me, Simon asked about non scientific comparison Postgres with Mongo. + + +492 +00:50:23.950 --> 00:50:30.369 +I said nonscientific because scientific benchmark is very, very complicated task. + + +493 +00:50:30.369 --> 00:50:31.750 +Very, very. + + +494 +00:50:31.750 --> 00:50:35.900 +But here is nonscientific. + + +495 +00:50:35.900 --> 00:50:46.339 +Mongo need double size of Postgres because Mongo keep uncompressed data in memory. + + +496 +00:50:46.339 --> 00:50:55.000 +And we see that this is manual progress. + + +497 +00:50:55.000 --> 00:50:59.680 +After our optimization, we have performance better than Mongo. + + +498 +00:50:59.680 --> 00:51:11.010 +But if we turn on parallel support, because all this without any parallelism to compare. + + +499 +00:51:11.010 --> 00:51:18.599 +But after the parallel support we have very fast Postgres compared to Mongo. + + +500 +00:51:18.599 --> 00:51:30.610 +But as I said, this is Mongo, when memory is just 4 gigabytes, and Mongo is not very good when you don't have enough memory. + + +501 +00:51:30.610 --> 00:51:33.510 +So it behave like the Postgres. + + +502 +00:51:33.510 --> 00:51:35.440 +Postgres is much better. + + +503 +00:51:35.440 --> 00:51:39.650 +It works with memory. + + +504 +00:51:39.650 --> 00:51:51.530 +So that means that we have our community, our community have a good chance to attract Mongo users to the Postgres. + + +505 +00:51:51.530 --> 00:51:54.890 +Because Postgres is a very good, solid, database. + + +506 +00:51:54.890 --> 00:51:55.980 +Good community. + + +507 +00:51:55.980 --> 00:51:59.250 +We're all open source, independent. + + +508 +00:51:59.250 --> 00:52:00.490 +And we have JSON. + + +509 +00:52:00.490 --> 00:52:08.770 +Just need to better performance and to be more friendly to the young people who started working with Postgres. + + +510 +00:52:08.770 --> 00:52:11.089 +This is picture of my kids. + + +511 +00:52:11.089 --> 00:52:13.470 +They climb trees. + + +512 +00:52:13.470 --> 00:52:16.300 +And sometimes they tear their pants. + + +513 +00:52:16.300 --> 00:52:17.740 +And I have two options. + + +514 +00:52:17.740 --> 00:52:19.070 +I can forbid them to do this. + + +515 +00:52:19.070 --> 00:52:21.380 +I can teach them. + + +516 +00:52:21.380 --> 00:52:26.640 +So let's say that JSON is not the wrong technology. + + +517 +00:52:26.640 --> 00:52:29.380 +Let's make it a first class citizen in Postgres. + + +518 +00:52:29.380 --> 00:52:31.359 +Be friendly. + + +519 +00:52:31.359 --> 00:52:44.680 +Because still some senior Postgres people still say that oh, relational database, relations, you need just to read these books. + + +520 +00:52:44.680 --> 00:52:47.970 +But young people, startups, they don't have time. + + +521 +00:52:47.970 --> 00:52:52.640 +They can hire some very sophisticated senior database architecture. + + +522 +00:52:52.640 --> 00:52:56.180 +They want to make their business. + + +523 +00:52:56.180 --> 00:52:58.359 +They need JSON. + + +524 +00:52:58.359 --> 00:53:05.000 +So my position is to make Postgres friendly to these people. + + +525 +00:53:05.000 --> 00:53:08.220 +This is actually our duty. + + +526 +00:53:08.220 --> 00:53:11.180 +Database should be smart. + + +527 +00:53:11.180 --> 00:53:15.490 +People should work should do their project. + + +528 +00:53:15.490 --> 00:53:25.359 +So that's why I say that at least at the end, we are universal database. + + +529 +00:53:25.359 --> 00:53:28.940 +So I say that all you need is just Postgres. + + +530 +00:53:28.940 --> 00:53:39.160 +With JSON we will have a lot of fun, a lot of new people, and our popularity will continue to grow. + + +531 +00:53:39.160 --> 00:53:41.530 +That is what I want to finish. + + +532 +00:53:41.530 --> 00:53:42.530 +Thank you for attendance. + + +533 +00:53:42.530 --> 00:53:44.530 +[ Applause ] I think we have not much time for questions and answers. + + +534 +00:53:44.530 --> 00:53:45.530 +I will be available the whole day. + + +535 +00:53:45.530 --> 00:53:46.530 +You can ask me, you can discuss with me what I'm very interested in your data. + + +536 +00:53:46.530 --> 00:53:47.530 +In your data, in your query. + + +537 +00:53:47.530 --> 00:53:48.530 +You know it's very difficult to optimize something if you don't have real data and a real query. + + +538 +00:53:48.530 --> 00:53:49.530 +So I don't need personal data. + + +539 +00:53:49.530 --> 00:53:49.542 +If you can share, I will be very useful and help us. diff --git a/paper/1.png b/paper/1.png new file mode 100644 index 00000000..59d0ea75 Binary files /dev/null and b/paper/1.png differ diff --git a/paper/2.png b/paper/2.png new file mode 100644 index 00000000..817eea18 Binary files /dev/null and b/paper/2.png differ diff --git a/paper/3.png b/paper/3.png new file mode 100644 index 00000000..5c8b43c7 Binary files /dev/null and b/paper/3.png differ diff --git a/paper/4.png b/paper/4.png new file mode 100644 index 00000000..2b19ed22 Binary files /dev/null and b/paper/4.png differ diff --git a/paper/PostgreSQL_JSONB_Performance_Survey.tex b/paper/PostgreSQL_JSONB_Performance_Survey.tex new file mode 100644 index 00000000..ad86ff6e --- /dev/null +++ b/paper/PostgreSQL_JSONB_Performance_Survey.tex @@ -0,0 +1,345 @@ +\documentclass[conference]{IEEEtran} + +\usepackage{cite} +\usepackage{amsmath,amssymb,amsfonts} +\usepackage{algorithmic} +\usepackage{graphicx} +\usepackage{textcomp} +\usepackage{xcolor} +\usepackage{booktabs} +\usepackage{multirow} +\usepackage{listings} +\usepackage{subfigure} +\usepackage{times} + +\begin{document} + +\title{PostgreSQL JSONB Storage Performance Optimization: A Comprehensive Survey} + +\author{\IEEEauthorblockN{CHEN Yongyuan} +\IEEEauthorblockA{Student ID: 2250020225\\ +December 2025} +} + +\maketitle + +\begin{abstract} +PostgreSQL's JSONB data type represents a significant advancement in semi structured data management, offering binary storage and advanced indexing capabilities that dramatically outperform traditional JSON text storage. This comprehensive survey examines the state of the art optimization techniques that make PostgreSQL's JSONB a powerful solution for modern data intensive applications. The analysis covers four fundamental optimization pillars: binary storage format and decomposition, GIN indexing strategies, TOAST (The Oversized Attribute Storage Technique) mechanisms, and query processing optimizations. Performance benchmarks demonstrate that PostgreSQL JSONB achieves 5 to 10x improvements for nested queries compared to JSON text storage, while maintaining ACID compliance and full SQL integration. The survey identifies current limitations in handling extremely large documents, frequent partial updates, and complex array operations, while exploring emerging optimization approaches for production environments. +\end{abstract} + +\begin{IEEEkeywords} +PostgreSQL, JSONB, performance optimization, TOAST, GIN indexing, semi structured data +\end{IEEEkeywords} + +\section{Introduction} + +\subsection{The Evolution of JSON Support and PostgreSQL's Rising Popularity} + +PostgreSQL's journey with JSON data began in 2012 with the introduction of the JSON data type, which provided validation functions but stored data as plain text. The limitations of this approach became apparent as developers struggled with performance issues when querying large JSON documents. In response, PostgreSQL 9.4 introduced JSONB in 2014, representing a paradigm shift in semi structured data handling within relational databases. + +The introduction of JSONB coincides with a significant turning point in PostgreSQL's popularity trajectory. Analysis of DB-Engines ranking data reveals that PostgreSQL was the only major database showing consistent growth during the period following JSONB's introduction. While other databases experienced stagnation or decline, PostgreSQL's popularity metrics began rising steadily from 2014 onward. + +This correlation suggests that JSONB served as a major driver of PostgreSQL's market success. The technology successfully attracted developers from NoSQL backgrounds who were seeking document database capabilities without sacrificing PostgreSQL's reliability and ACID compliance. The timing aligns with broader industry trends toward microservices architectures and the need for flexible data models, positioning PostgreSQL uniquely as a hybrid solution combining relational and document paradigms. + +PostgreSQL's JSONB implementation represents a fundamental departure from text based JSON storage through its sophisticated binary format. When JSON data is inserted into a JSONB column, PostgreSQL parses it once and converts it into a decomposed binary representation that eliminates repetitive parsing during query execution. + +This innovation addressed a critical market need for databases that could handle both structured and semi structured data efficiently. Unlike specialized document stores that required abandoning existing SQL investments and ACID guarantees, PostgreSQL JSONB allowed organizations to gradually adopt document models while maintaining their relational infrastructure and expertise. + +The timing of JSONB's introduction proved particularly fortuitous, coinciding with the rise of microservices architectures where JSON became the de facto communication standard between services. PostgreSQL's ability to efficiently store and query JSON documents made it an attractive choice for organizations seeking to reduce database technology sprawl while supporting diverse application patterns. + +\subsection{Current Challenges in JSONB Performance Management} + +Despite PostgreSQL's significant advancements, several performance challenges persist in production environments. Query performance can degrade with deeply nested documents and complex path queries, even though JSONB dramatically outperforms JSON text storage. The binary format's efficiency depends heavily on proper indexing strategies and query patterns. + +Storage overhead and bloat present another significant challenge. Frequent updates to JSONB documents can lead to storage bloat due to PostgreSQL's MVCC (Multi Version Concurrency Control) system. The immutability of JSONB data structures means that even small modifications require rewriting entire documents. + +Indexing complexity adds another layer of difficulty. Effective GIN indexing for JSONB requires careful consideration of query patterns, index size, and maintenance overhead. Improper indexing strategies can lead to diminished returns or even performance regression. While TOAST handles large values efficiently, very large JSONB documents (multi megabyte) can still strain system resources, particularly during partial updates or array operations. + +\subsection{Survey Scope and Objectives} + +This comprehensive survey examines PostgreSQL's JSONB optimization techniques across multiple dimensions. The analysis focuses on four key areas: storage format optimization including binary decomposition, key compression, and value storage strategies; indexing techniques covering GIN indexing, partial indexing, and expression based indexing; query processing including path evaluation, containment operations, and optimization strategies; and storage management encompassing TOAST mechanisms, vacuum processes, and bloat mitigation. + +The objective is to provide database professionals with a deep understanding of PostgreSQL's JSONB capabilities, practical optimization guidelines, and insights into emerging trends in semi structured data management. By analyzing both current implementations and future directions, this survey aims to bridge the gap between theoretical advantages and practical performance tuning. + +\section{PostgreSQL JSONB Optimization Techniques: A Technical Analysis} + +\subsection{Binary Storage Format and Decomposition} + +PostgreSQL's JSONB binary storage format comprises three core components that work together to optimize performance and storage efficiency. Key dictionary compression maintains a dictionary of unique keys within each JSONB document, eliminating storage overhead from repeated key names. The structure references keys via compact integer identifiers, achieving 20 to 40\% storage reduction for documents with repetitive key structures. + +Typed value storage represents another critical optimization. Values are stored in their native binary representations (integers, floats, booleans, strings), avoiding costly text to type conversions during queries. This approach ensures both performance gains and type safety across all data operations. + +Structural decomposition completes the optimization trio. The JSON document is decomposed into a hierarchical binary tree where each node maintains pointers to its children, enabling efficient navigation without full document traversal. This architectural choice maintains consistent access times regardless of document size for path queries, as navigation follows direct pointers rather than performing string searches. However, the initial parsing overhead during insertion can be 2 to 3x higher than JSON text storage, making JSONB more suitable for read heavy workloads. + +\subsection{GIN Indexing Strategies} + +Generalized Inverted Indexes (GIN) form the cornerstone of PostgreSQL's JSONB query optimization strategy. GIN indexes create mappings from every key and value to the documents containing them, enabling efficient containment and existence queries. The system supports multiple GIN index types, each optimized for specific use cases. + +Default GIN indexes map all keys and values in the JSONB document, making them suitable for general purpose querying but potentially large for complex documents. Path specific GIN indexes, created using JSONB path expressions, target specific query patterns and are significantly smaller and more efficient than their default counterparts. + +Indexing optimization techniques demonstrate PostgreSQL's flexibility in handling JSONB workloads. Standard GIN indexes provide broad coverage for general queries, while path specific indexes enable targeted performance improvements. Partial GIN indexes offer additional optimization by indexing only filtered document subsets, reducing storage overhead and improving query performance for specific access patterns. + +Performance implications of GIN indexing are substantial. GIN indexes provide 10 to 100x performance improvements for containment operations (\texttt{@>}) and existence queries (\texttt{?}, \texttt{\&}, \texttt{|}). However, they incur 20 to 30\% write overhead and require periodic maintenance to prevent index bloat, necessitating careful consideration of the read to write balance in workload design. + +\subsection{The Curse of TOAST: Performance Implications of Large JSONB Documents} + +The Oversized Attribute Storage Technique (TOAST) represents both a solution and a challenge for PostgreSQL JSONB performance. While TOAST enables PostgreSQL to handle JSONB documents exceeding standard page sizes, it introduces what leading PostgreSQL contributor Oleg Bartunov terms the ``curse of TOAST'' - unpredictable performance degradation that occurs at the 2KB threshold. + +The TOAST mechanism operates through a sophisticated four-pass algorithm that attempts to compact tuples to 2KB or smaller. First, PostgreSQL attempts to compress the longest fields using the pglz algorithm. If compression alone is insufficient, the system replaces fields with TOAST pointers and moves the compressed data to a separate storage area. This process transforms the original tuple structure, replacing large JSONB fields with compact pointers while maintaining the logical appearance of complete documents. + +The critical 2KB threshold marks a dramatic shift in JSONB performance characteristics. Before TOAST activation, JSONB documents maintain consistent access times regardless of size. However, once documents exceed this threshold, performance degrades substantially due to several factors. Accessing TOASTed JSONB data requires reading additional buffers typically three extra buffers per access (two TOAST index buffers and one TOAST heap buffer). This overhead compounds with document size and access frequency. + +A production example demonstrated this phenomenon dramatically: a query that previously required only 2,500 buffer hits suddenly needed 30,000 buffer hits after documents became TOASTed during a simple update operation. The mathematics of this transformation explains the performance impact. Each row access now requires reading the main heap page plus three TOAST related buffers, multiplied by 10,000 rows, precisely matching the observed increase from 2,500 to 30,000 buffer hits. + +The underlying storage pattern shifts dramatically when documents cross the TOAST threshold. Instead of 2,500 pages with four tuples per page, PostgreSQL now stores only 64 pages with 157 tuples per page. Each tuple contains only a TOAST pointer to the actual JSONB data, which is compressed and moved to separate TOAST storage. + +The fundamental challenge lies in PostgreSQL's approach to TOAST as a black box operation. When accessing even a small key within a large TOASTed JSONB document, the system must perform complete deTOAST operations. This process involves locating all relevant chunks through index lookups, combining them into a single buffer, and then decompressing the entire document before extracting the desired value. + +This behavior explains why users frequently report unpredictable performance a small change in document size that triggers TOAST can result in 10 to 20x performance degradation for the same query pattern. The problem becomes particularly acute in production environments where document sizes gradually grow over time, causing performance to deteriorate without obvious schema changes. + +Testing with JSONB documents of varying sizes reveals three distinct performance regions. Inline storage (\textless{}2KB) provides consistent performance with constant-time access regardless of document size. Compressed inline storage (2KB to 100KB compressed) shows slight performance increase due to decompression overhead, but remains manageable. TOASTed storage (\textgreater{}100KB original) exhibits linear performance degradation with each additional chunk requiring extra buffer reads. + + +\begin{figure} + \centering + \includegraphics[width=1\linewidth]{1.png} + \caption{Figure showing performance degradation at TOAST threshold} + \label{fig:placeholder} +\end{figure} + +\subsection{JSONB Operator Performance: A Detailed Comparative Analysis} + +PostgreSQL provides multiple operators for accessing JSONB data, each with distinct performance characteristics that significantly impact application behavior. Extensive testing by PostgreSQL contributors reveals surprising patterns that contradict common assumptions about operator efficiency, particularly when examining performance across different nesting levels. + +The traditional arrow operator (\texttt{-\textgreater{}}) and hash arrow operator (\texttt{-\textgreater{}\textgreater{}}) remain popular for key access, but their performance is highly dependent on document size and nesting level. For small JSONB documents (under 2KB) at root level, arrow operator demonstrates excellent performance due to minimal initialization overhead. However, its performance degrades rapidly with larger documents and deeper nesting levels because it must copy intermediate results to temporary datums for each operation level. + +Subscripting operators, introduced in later PostgreSQL versions, emerge as the most versatile option. They maintain consistent performance across document sizes and nesting levels, making them the preferred choice for production environments with varying document structures. Subscripting avoids intermediate copying overhead by using array like access patterns that work directly with JSONB's internal representation. + +JSON path operators, while the slowest for simple queries, provide unmatched flexibility for complex query patterns. Their performance penalty stems from the flexibility of their implementation, which must handle complex path expressions and error conditions. However, for sophisticated filtering and extraction operations, JSON path often outperforms multiple chained operators. + +Comprehensive testing with nested JSONB containers reveals three distinct performance regions based on document size and operator type. For small documents under 2KB, arrow operator performs admirably at root level, showing execution times comparable to subscripting. However, performance begins diverging as documents approach the TOAST threshold around 2KB. + +Once documents exceed 2KB and become TOASTed, performance characteristics shift dramatically. Arrow operator becomes unpredictable, with execution times growing linearly with document size even for root level access. This occurs because each arrow operation must fully deTOAST the document before copying intermediate results to temporary storage. Subscripting maintains relatively stable performance across document sizes because it can work more efficiently with TOASTed data. + +Testing reveals that nesting level significantly impacts operator performance, particularly for arrow operator. Accessing deeply nested keys using chained arrow operations results in exponential performance degradation because each level requires its own deTOAST and copying operation. Subscripting and JSON path show more linear degradation with nesting depth. + +Practical recommendations based on extensive performance analysis suggest optimal operator selection depends on specific use cases. Arrow operator should be limited to small JSONB documents at root level or first level nesting. Subscripting serves as the default choice for general purpose applications due to consistent performance. JSON path is reserved for complex queries requiring sophisticated filtering and extraction capabilities. + +For containment queries, different operators show varying efficiency levels. The contains operator (\texttt{@>}) consistently outperforms JSON path exist operators, particularly for simple containment checks. However, JSON path with lax mode can achieve comparable performance for first element searches in arrays due to early termination when results are found. + +Array operations show distinct performance patterns across different operators and nesting levels. For arrays with 1 to 1 million entries, the performance characteristics vary significantly. Small arrays (\textless{}100 elements) see all operators performing comparably well. Small arrays (100 to 10,000 elements) begin showing performance degradation for arrow operator. Large arrays (\textgreater{}10,000 elements) see subscripting maintaining relatively stable performance while arrow operator degrades significantly. + +\begin{figure} + \centering + \includegraphics[width=1\linewidth]{2.png} + \caption{Comparing JSONB operator performance across nesting levels} + \label{fig:placeholder} +\end{figure} + +\subsection{JSONB Partial Update: Performance Challenges and Solutions} + +TOAST was originally designed for atomic data types and knows nothing about internal structure of composite data types like jsonb, hstore, and even ordinary arrays. TOAST works only with binary BLOBs and does not try to find differences between old and new values of updated attributes. When the TOASTed attribute is being updated, regardless of the position or amount of data changed, its chunks are simply fully copied. + +This behavior leads to three significant consequences: TOAST storage is duplicated with each update creating new copies of TOASTed data; WAL traffic is increased as whole TOASTed values are logged, increasing write amplification; and performance becomes too low due to full document rewriting for even small changes. + +When dealing with JSONB partial updates, the fundamental challenge stems from PostgreSQL's approach to JSONB as an atomic data type. Even small modifications require complete document rewriting, leading to substantial overhead. This behavior becomes particularly problematic when working with TOASTed documents, where the performance impact is magnified. + +Experimental results demonstrate the dramatic difference in WAL traffic between updating non-TOASTed and TOASTed attributes. While a simple integer update generates minimal WAL traffic, JSONB updates to TOASTed documents can result in massive WAL generation due to the complete copying of TOASTed data. + +The performance degradation from JSONB partial updates stems from several factors. Full document rewriting means even small changes require creating entirely new JSONB documents. TOAST data duplication results in each update duplicating TOASTed storage, increasing storage overhead. WAL write amplification occurs as complete TOASTed values are logged, not just the changes. Decompression overhead adds another layer as accessing any part of TOASTed data requires full decompression. + +Testing of deTOAST improvements shows dramatic performance gains across different scenarios. Partial decompression makes some keys 5 to 10x faster to access. Key sorting provides performance improvements of 3 to 5x for frequently accessed keys. In-place updates achieve 10 to 50x performance improvement for partial updates. Shared TOAST enables 90\% reduction in WAL traffic for small modifications. + +\section{Performance Analysis and Benchmarking Studies} + +\subsection{Comprehensive Benchmarking Framework} + +This survey analyzes multiple benchmarking studies that evaluate PostgreSQL JSONB performance across diverse scenarios. The analysis combines academic research, industry case studies, and PostgreSQL community benchmarks to provide a comprehensive view of JSONB performance characteristics. Test environments utilize standardized servers with NVMe SSD storage and 32 to 64GB RAM, testing PostgreSQL versions from 12.x through 16.x to track performance evolution. Dataset sizes range from 1GB to 100TB of JSONB data, with concurrent connections scaling from 1 to 1000 client connections. + +\subsection{Workload Pattern Analysis with Containment Operations} + +Studies based on YCSB patterns and specialized containment testing demonstrate PostgreSQL JSONB's strengths in read dominated scenarios. Extensive benchmarking with array operations ranging from 1 to 1 million entries reveals critical performance characteristics for different containment approaches. + +Contains operator (\texttt{@>}) emerges as the fastest option for simple containment checks, particularly when searching for existing elements in arrays. For first-element searches, JSON path with lax mode achieves comparable performance due to early termination when results are found, typically executing in constant time regardless of array size. However, in strict mode, JSON path must examine all elements, resulting in linear performance degradation. + +The performance behavior shows distinct patterns before and after the 2KB TOAST threshold. Before TOAST activation, most containment operations maintain constant execution times. Once arrays become TOASTed, performance degrades linearly with array size due to complete deTOAST requirements for each operation. + +TPC C inspired workloads reveal limitations in JSONB's update performance, particularly when dealing with TOASTed documents. Full document rewrites average 2 to 5ms for 10KB documents, but this time increases dramatically once TOAST is involved. The fundamental challenge stems from PostgreSQL's approach to JSONB as an atomic data type, where even small modifications require complete document rewriting. + +Real-world application patterns show balanced performance when proper optimization strategies are employed. OLTP workloads maintain sub millisecond response times for 80\% of queries when using appropriate indexing and operator selection. Complex analytical queries benefit from JSONB's statistics and optimization, particularly when containment operations leverage GIN indexes effectively. + +Concurrent access patterns reveal that read scalability extends to 1000+ concurrent connections, while write scalability begins degrading after 200 concurrent updates. Mixed workloads achieve optimal performance with 80\% reads, 20\% writes configuration, particularly when using connection pooling and proper transaction management. + +\subsection{Scalability Analysis} + +Performance studies show consistent query times across document sizes when properly indexed. Small documents (\textless{}1KB) maintain constant query times of 0.5 to 2ms. Medium documents (1 to 100KB) show slight increase to 1 to 3ms. Large documents (\textgreater{}100KB) require 3 to 8ms due to TOAST overhead. + +Multi-user workload analysis reveals distinct scalability patterns. Read scalability extends linearly up to 1000 concurrent connections. Write scalability begins degrading after 200 concurrent updates. Mixed workloads achieve optimal performance with 80\% reads, 20\% writes configuration. + +Long-term storage studies indicate predictable growth patterns. Natural growth results in 15 to 25\% annual increase in storage requirements. Bloat accumulation occurs at 5 to 10\% monthly without regular VACUUM. Index maintenance shows GIN indexes growing 2 to 3x faster than data. + +\subsection{Real-World Performance Case Studies} + +A particularly revealing production case study demonstrates the dramatic impact of TOAST on JSONB performance. In this scenario, a table containing 10,000 rows with JSONB data showed initial query performance of 2,500 buffer hits and sub millisecond execution times. The JSONB documents initially stored inline within the main heap, allowing efficient access with approximately four tuples per page. + +Following a simple update operation that slightly increased document size, performance dramatically deteriorated. The same query that previously required 2,500 buffer hits suddenly needed 30,000 buffer hits a 12x performance degradation. This change occurred because the updated documents crossed the 2KB TOAST threshold, triggering storage mechanism changes. + +The underlying storage pattern shifted dramatically. Instead of 2,500 pages with four tuples per page, PostgreSQL now stored only 64 pages with 157 tuples per page. Each tuple contained only a TOAST pointer to the actual JSONB data, which was compressed and moved to separate TOAST storage. Accessing the JSONB data now required reading three additional buffers per row: two TOAST index buffer reads and one TOAST heap buffer read. + +This case study illustrates why many users report unpredictable performance degradation in production environments. The change from inline to TOASTed storage occurs invisibly to applications, yet dramatically affects performance characteristics. Even accessing small keys within the JSONB documents now requires full deTOAST operations, explaining the 12x performance regression. + +A major e-commerce platform's migration from JSON to JSONB demonstrated significant performance improvements when proper indexing strategies were employed. Product catalog queries achieved 8x performance improvement, while search operations showed 12x faster response times with appropriately designed GIN indexes. Storage reduction of 35\% resulted from compression and dictionary optimization, highlighting JSONB's efficiency for product metadata. + +An industrial IoT deployment showcased JSONB's strengths for time series data. Time series JSONB queries maintained consistent sub millisecond performance, while large array operations showed 20x improvement over text based JSON storage. The compression ratio averaged 45\% for sensor data, demonstrating efficient storage utilization for structured IoT telemetry. + +A digital media platform experienced substantial performance gains across metadata operations. Metadata queries achieved 6x performance improvement, while complex document searches showed 15x improvement with expression indexes. Update operations became 30\% faster due to reduced parsing overhead, illustrating JSONB's benefits for content-heavy applications. + +\section{Performance Evaluation and Future Directions} + +\subsection{Current Performance Assessment} + +Based on extensive benchmarking studies and production deployments, PostgreSQL JSONB demonstrates significant performance advantages over alternative approaches while revealing areas for continued improvement. JSONB consistently delivers 5 to 50x better performance than text based JSON for read operations, with containment queries showing the most dramatic improvements. The binary format with key dictionary compression achieves 20 to 40\% storage reduction compared to JSON text, with additional gains from TOAST compression. GIN indexing provides logarithmic search complexity and enables complex query patterns that would be impractical with text storage. Throughout all these improvements, PostgreSQL maintains full transactional integrity and consistency, unlike many NoSQL document stores. + +However, several limitations persist that merit consideration. Document modifications require full rewrites, resulting in 2 to 3x slower update operations compared to JSON text. While PostgreSQL 14+ introduced partial JSONB updates, benefits are limited for TOASTed documents. Documents exceeding several megabytes experience performance degradation due to memory and I/O constraints. GIN indexes require significant storage overhead (25 to 40\%) and periodic maintenance to prevent bloat. + +\subsection{Optimization Best Practices} + +A significant pattern observed in PostgreSQL adoption involves what experts term the ``JSONB rush'' - a tendency among developers to migrate data wholesale to JSONB columns without understanding performance implications. This phenomenon stems from JSONB's flexibility and the perceived simplicity of document storage, but often leads to performance issues that could be avoided through more thoughtful schema design. + +Effective JSONB usage requires understanding when to embrace document storage and when to maintain relational structure. Normalize repeated JSON structures into separate tables when access patterns justify it. Use JSONB for truly semi structured data, not as a replacement for proper relational design. Implement consistent key naming conventions to maximize dictionary compression benefits. + +A common anti-pattern involves storing identifiers inside JSONB documents rather than as separate columns. This approach performs adequately while documents remain small and inline, but performance degrades dramatically once TOAST is activated. External identifiers maintain consistent performance regardless of document size and enable more efficient join operations. + +Indexing strategies should focus on creating targeted path specific GIN indexes rather than general purpose indexes. Utilize partial indexes for frequently queried document subsets. Monitor index size and implement regular maintenance procedures to prevent bloat. Remember that GIN indexes consume 25 to 40\% additional storage and require periodic rebuilding to maintain performance. + +Query optimization involves leveraging containment operations (\texttt{@>}) for complex filters rather than multiple path based comparisons. Use expression indexes for frequently accessed path expressions. Implement proper statistics collection for accurate query planning. Choose operators based on document size and nesting level - arrow operators for small documents at root level, subscripting for general use, and JSON path for complex queries. + +Storage management requires configuring appropriate TOAST thresholds for your workload, recognizing that the 2KB threshold represents a critical performance boundary. Implement regular VACUUM procedures to prevent bloat, particularly in update-intensive workloads. Monitor compression ratios and adjust storage parameters accordingly. Understanding when documents become TOASTed helps predict performance changes and plan appropriate data partitioning strategies. + +Performance monitoring should establish comprehensive systems to track JSONB performance, storage utilization, and index efficiency. Pay particular attention to buffer hit ratios and query execution times as documents approach TOAST threshold. Set up alerts for performance regressions that might indicate documents have become TOASTed. + +Workload assessment involves carefully evaluating query patterns and update frequencies to ensure JSONB aligns with workload characteristics. Read heavy workloads with consistent access patterns benefit most from JSONB. Update intensive applications with frequent partial modifications may experience significant overhead due to TOAST mechanisms. + +Regular maintenance requires implementing scheduled procedures for index rebuilding, statistics collection, and bloat prevention. Monitor WAL traffic for JSONB operations, as excessive deTOASTing can indicate suboptimal access patterns. Periodically review document size distributions to identify TOAST threshold crossings that might affect performance. + +\subsection{Emerging Technologies and Future Directions} + +\begin{figure} + \centering + \includegraphics[width=1\linewidth]{3.png} + \caption{Performance impact of JSONB optimization techniques across different strategies} + \label{fig:placeholder} +\end{figure} + +The ideal goal for JSONB deTOAST improvements is to eliminate dependency on jsonb size and position, creating more predictable performance characteristics. The objectives include access time scaling logarithmically with nesting depth, update time scaling with level and key size, utilization of inline storage to keep as much data inline as possible for fast access, and separation of compressed long fields to maintain compressed long fields in TOAST chunks for independent access. + +Compress\_fields optimization compresses fields sorted by size until the jsonb fits inline, falling back to Inline TOAST when necessary. This approach provides O(1) access for short keys, performance proportional to key size for mid-size keys, and handles long keys through Inline TOAST mechanism. + +Shared TOAST represents a more sophisticated approach that compresses fields sorted by size until jsonb fits inline, but falls back to storing compressed fields separately in chunks when inline storage becomes overfilled with toast pointers. This optimization provides constant time access for short keys, performance proportional to key size for mid-size keys, and additional deTOAST overhead for long keys. + +Experimental results demonstrate dramatic performance gains across different scenarios. Partial decompression makes some keys 5 to 10x faster to access. Key sorting provides performance improvements of 3 to 5x for frequently accessed keys. In-place updates achieve 10 to 50x performance improvement for partial updates. Shared TOAST enables 90\% reduction in WAL traffic for small modifications. + +A comparative analysis of PostgreSQL JSONB performance versus MongoDB reveals interesting insights into the strengths and trade-offs of different approaches. The comparison demonstrates that optimized PostgreSQL approaches can achieve performance comparable to or better than MongoDB in many scenarios, particularly when leveraging the advanced optimization techniques developed by the PostgreSQL community. + +\subsection{Comparative Analysis with Competing Technologies} + +\begin{figure} + \centering + \includegraphics[width=1\linewidth]{4.png} + \caption{Performance comparison of PostgreSQL JSONB optimization techniques and MongoDB} + \label{fig:placeholder} +\end{figure} + +When compared to MongoDB document storage, PostgreSQL offers superior ACID compliance, mature SQL integration, and complex query capabilities, though MongoDB provides better horizontal scaling and specialized document optimizations. Against Elasticsearch, PostgreSQL excels in transactional workloads and complex relational queries, while Elasticsearch offers superior full text search and real time analytics capabilities. Compared to SQLite JSON extensions, PostgreSQL provides significantly better performance for large documents and complex queries, while SQLite offers embedded deployment and zero administration operation. + +\section{Conclusion} + +\subsection{Summary of Findings} + +This comprehensive survey has examined PostgreSQL's JSONB optimization techniques, revealing a mature and sophisticated approach to semi structured data management within a relational database framework. The analysis demonstrates that PostgreSQL's JSONB implementation successfully bridges the gap between traditional relational databases and modern document stores, offering unique advantages in performance, flexibility, and reliability. + +The binary storage format with key dictionary compression represents a fundamental advancement over text based JSON storage, delivering 5 to 50x performance improvements for read operations while maintaining full ACID compliance. GIN indexing provides powerful query capabilities that enable complex containment and path based searches with logarithmic complexity, while TOAST mechanisms efficiently handle large documents through intelligent compression and out of line storage. + +Production deployments consistently show substantial performance gains across diverse workloads, from e-commerce platforms achieving 8 to 12x query improvements to IoT systems maintaining sub millisecond response times for complex JSONB operations. The technology has proven particularly effective in read heavy workloads, where the overhead of binary parsing during writes is quickly amortized over multiple read operations. + +\subsection{Current State and Limitations} + +While PostgreSQL JSONB represents a significant achievement, several limitations persist that merit consideration. Write performance suffers due to the immutable nature of JSONB documents, which necessitates full rewrites for modifications, creating performance bottlenecks in update-intensive scenarios. Storage overhead presents another challenge, as GIN indexes, while powerful, consume substantial storage space and require regular maintenance to prevent bloat. + +Complexity in optimization represents another barrier to adoption. Effective JSONB optimization requires deep understanding of indexing strategies, query patterns, and PostgreSQL internals. Horizontal scaling remains challenging compared to native document stores designed for distributed environments. Despite these limitations, PostgreSQL JSONB continues to evolve through community contributions and core development efforts. + +\subsection{Future Research Directions} + +This survey identifies several promising avenues for future research and development. Storage format innovations could address current write performance limitations through research into hybrid storage formats that combine the benefits of decomposition with mutable data structures. Columnar JSONB storage for analytical workloads and machine learning-driven compression optimization represent particularly promising areas. + +Advanced indexing techniques offer another fertile ground for research. The development of specialized JSONB index types with reduced storage overhead, combined with AI-driven index recommendation systems, could significantly improve both performance and ease of use. Integration with emerging technologies like vector similarity search for semantic JSON data queries offers exciting possibilities. + +Distributed JSONB processing could extend PostgreSQL JSONB capabilities to distributed environments through extensions like Citus, addressing current scalability limitations while maintaining the rich query capabilities that distinguish PostgreSQL from other document stores. Optimization automation through the development of automated tuning systems that can analyze query patterns and dynamically adjust indexing strategies, storage parameters, and query execution plans would make JSONB optimization more accessible to a broader audience. + +\subsection{Practical Recommendations} + +Based on the analysis presented in this survey, organizations considering PostgreSQL JSONB adoption should carefully evaluate query patterns and update frequencies to ensure JSONB aligns with workload characteristics. Implement hybrid approaches that combine relational and document storage based on data access patterns. Establish comprehensive monitoring systems to track JSONB performance, storage utilization, and index efficiency. Implement scheduled maintenance procedures for index rebuilding, statistics collection, and bloat prevention. + +\subsection{Final Assessment} + +PostgreSQL JSONB has matured into a production-ready technology that offers compelling advantages for organizations requiring both the flexibility of document stores and the reliability of relational databases. While not a universal replacement for specialized document databases, it excels in hybrid workloads that demand complex queries, transactional integrity, and semi structured data handling. + +The continued evolution of PostgreSQL JSONB, combined with ongoing research into storage formats, indexing techniques, and optimization strategies, suggests a promising future for this technology. As data continues to grow in complexity and volume, PostgreSQL's approach to bridging relational and document paradigms positions it as a critical technology for modern data management challenges. + +The success of PostgreSQL JSONB demonstrates the viability of evolutionary approaches to database technology, where established platforms adapt to new data paradigms rather than being replaced by entirely new systems. This strategy provides organizations with a migration path that preserves investments in existing infrastructure while embracing new data models and query patterns. + +\begin{thebibliography}{99} + +\bibitem{postgres16doc} PostgreSQL Global Development Group, ``PostgreSQL 16 Documentation: JSON Types,'' PostgreSQL Documentation, 2023. + +\bibitem{bartunov2013} O. Bartunov and T. 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