is00hcw / redis-sampler Goto Github PK
View Code? Open in Web Editor NEWThis project forked from antirez/redis-sampler
Small program to understand the composition of your Redis data set
License: BSD 3-Clause "New" or "Revised" License
redis-sampler's Introduction
Redis Sampler README
====================
Redis sampler is an utility to check the composition of your Redis dataset.
Using it is as simple as typing:
./redis-sampler.rb <host> <port> <db> <samplesize>
The host and port arguments are the ones of your Redis instance.
The DB is the database to test. By default Redis uses database 0 unless you
are writing against a different DB.
The sample size is the number of elements to check using RANDOMKEY.
For large datasets a sample size of 100000 or more is recommended.
To understand how the program works you can try a few runs setting the
sampling size to just 1000, for fast execution.
It is highly recommended to run the program via loopback interface or fast
LAN, otherwise the execution time will be pretty high.
Example Output
==============
Sampling localhost:6379 DB:4 with 1000000 RANDOMKEYS
TYPES
=====
zset: 873268 (87.33%) string: 124995 (12.50%) set: 1022 (0.10%)
hash: 576 (0.06%) list: 139 (0.01%)
STRINGS, SIZE OF VALUES
=======================
6: 61222 (48.98%) 7: 17056 (13.65%) 13: 8274 (6.62%)
15: 7991 (6.39%) 5: 4629 (3.70%) 31: 3263 (2.61%)
20: 2670 (2.14%) 2: 2518 (2.01%) 27: 1675 (1.34%)
42: 1270 (1.02%) 159: 893 (0.71%) 1: 705 (0.56%)
47: 641 (0.51%) 34: 594 (0.48%) 41: 521 (0.42%)
38: 493 (0.39%) 28: 413 (0.33%) 22: 406 (0.32%)
139: 351 (0.28%) 29: 343 (0.27%) 83: 337 (0.27%)
(suppressed 172 items with perc < 0.5% for a total of 6.98%)
Average: 15.97 Standard Deviation: 26.52
Min: 0 Max: 1123
Powers of two distribution: (NOTE <= p means: p/2 < x <= p)
<= 8: 82913 (66.33%) <= 16: 16789 (13.43%) <= 32: 9571 (7.66%)
<= 64: 6232 (4.99%) <= 128: 3333 (2.67%) <= 256: 2682 (2.15%)
<= 2: 2518 (2.01%) <= 1: 740 (0.59%) <= 4: 199 (0.16%)
<= 512: 14 (0.01%) <= 1024: 3 (0.00%) <= 2048: 1 (0.00%)
LISTS, NUMBER OF ELEMENTS
=========================
2: 28 (20.14%) 5: 18 (12.95%) 9: 10 (7.19%)
8: 9 (6.47%) 11: 9 (6.47%) 13: 7 (5.04%)
12: 7 (5.04%) 14: 6 (4.32%) 15: 6 (4.32%)
4: 4 (2.88%) 16: 4 (2.88%) 21: 4 (2.88%)
27: 3 (2.16%) 7: 3 (2.16%) 10: 3 (2.16%)
19: 2 (1.44%) 1: 2 (1.44%) 25: 2 (1.44%)
41: 1 (0.72%) 3: 1 (0.72%) 17: 1 (0.72%)
(suppressed 9 items with perc < 0.5% for a total of 6.47%)
Average: 10.58 Standard Deviation: 8.58
Min: 1 Max: 42
Powers of two distribution: (NOTE <= p means: p/2 < x <= p)
<= 16: 52 (37.41%) <= 8: 30 (21.58%) <= 2: 28 (20.14%)
<= 32: 17 (12.23%) <= 64: 5 (3.60%) <= 4: 5 (3.60%)
<= 1: 2 (1.44%)
LISTS, SIZE OF ELEMENTS
=======================
7: 106 (76.26%) 6: 33 (23.74%)
Average: 6.76 Standard Deviation: 0.43
Min: 6 Max: 7
Powers of two distribution: (NOTE <= p means: p/2 < x <= p)
<= 8: 139 (100.00%)
SETS, NUMBER OF ELEMENTS
========================
1: 216361 (24.78%) 2: 106871 (12.24%) 3: 67648 (7.75%)
4: 48207 (5.52%) 5: 36085 (4.13%) 6: 29597 (3.39%)
7: 23765 (2.72%) 8: 22549 (2.58%) 9: 20143 (2.31%)
10: 18069 (2.07%) 11: 16387 (1.88%) 12: 15009 (1.72%)
13: 13869 (1.59%) 14: 12683 (1.45%) 15: 12319 (1.41%)
16: 10794 (1.24%) 17: 10068 (1.15%) 18: 8925 (1.02%)
19: 8007 (0.92%) 20: 7618 (0.87%) 22: 7240 (0.83%)
21: 7055 (0.81%) 23: 5973 (0.68%) 24: 5771 (0.66%)
25: 4934 (0.57%)
(suppressed 2061 items with perc < 0.5% for a total of 15.72%)
Average: 1.24 Standard Deviation: 2.34
Min: 1 Max: 62
Powers of two distribution: (NOTE <= p means: p/2 < x <= p)
<= 1: 975 (95.40%) <= 2: 21 (2.05%) <= 8: 10 (0.98%)
<= 4: 9 (0.88%) <= 16: 5 (0.49%) <= 32: 1 (0.10%)
<= 64: 1 (0.10%)
SETS, SIZE OF ELEMENTS
======================
19: 871 (85.23%) 3: 66 (6.46%) 4: 65 (6.36%)
5: 11 (1.08%) 2: 9 (0.88%)
Average: 16.71 Standard Deviation: 5.50
Min: 2 Max: 19
Powers of two distribution: (NOTE <= p means: p/2 < x <= p)
<= 32: 871 (85.23%) <= 4: 131 (12.82%) <= 8: 11 (1.08%)
<= 2: 9 (0.88%)
SORTED SETS, NUMBER OF ELEMENTS
===============================
1: 216361 (24.78%) 2: 106871 (12.24%) 3: 67648 (7.75%)
4: 48207 (5.52%) 5: 36085 (4.13%) 6: 29597 (3.39%)
7: 23765 (2.72%) 8: 22549 (2.58%) 9: 20143 (2.31%)
10: 18069 (2.07%) 11: 16387 (1.88%) 12: 15009 (1.72%)
13: 13869 (1.59%) 14: 12683 (1.45%) 15: 12319 (1.41%)
16: 10794 (1.24%) 17: 10068 (1.15%) 18: 8925 (1.02%)
19: 8007 (0.92%) 20: 7618 (0.87%) 22: 7240 (0.83%)
21: 7055 (0.81%) 23: 5973 (0.68%) 24: 5771 (0.66%)
25: 4934 (0.57%)
(suppressed 2061 items with perc < 0.5% for a total of 15.72%)
Average: 25.47 Standard Deviation: 110.64
Min: 1 Max: 7018
Powers of two distribution: (NOTE <= p means: p/2 < x <= p)
<= 1: 216361 (24.78%) <= 16: 119273 (13.66%) <= 4: 115855 (13.27%)
<= 8: 111996 (12.82%) <= 2: 106871 (12.24%) <= 32: 92814 (10.63%)
<= 64: 52715 (6.04%) <= 128: 29286 (3.35%) <= 256: 13988 (1.60%)
<= 512: 7098 (0.81%) <= 1024: 4762 (0.55%) <= 2048: 1840 (0.21%)
<= 4096: 393 (0.05%) <= 8192: 16 (0.00%)
SORTED SETS, SIZE OF ELEMENTS
=============================
6: 710230 (81.33%) 5: 75292 (8.62%) 4: 68661 (7.86%)
3: 17136 (1.96%) 2: 1412 (0.16%) 9: 253 (0.03%)
1: 76 (0.01%) 8: 74 (0.01%) 7: 39 (0.00%)
21: 6 (0.00%) 30: 6 (0.00%) 20: 6 (0.00%)
23: 6 (0.00%) 26: 5 (0.00%) 34: 5 (0.00%)
24: 4 (0.00%) 27: 4 (0.00%) 18: 4 (0.00%)
15: 4 (0.00%) 38: 3 (0.00%) 43: 3 (0.00%)
(suppressed 24 items with perc < 0.5% for a total of 0.00%)
Average: 5.69 Standard Deviation: 0.77
Min: 1 Max: 63
Powers of two distribution: (NOTE <= p means: p/2 < x <= p)
<= 8: 785635 (89.96%) <= 4: 85797 (9.82%) <= 2: 1412 (0.16%)
<= 16: 262 (0.03%) <= 1: 76 (0.01%) <= 32: 58 (0.01%)
<= 64: 28 (0.00%)
HASHES, NUMBER OF FIELDS
========================
1: 301 (52.26%) 12: 177 (30.73%) 11: 95 (16.49%)
13: 2 (0.35%) 14: 1 (0.17%)
Average: 6.09 Standard Deviation: 5.34
Min: 1 Max: 14
Powers of two distribution: (NOTE <= p means: p/2 < x <= p)
<= 1: 301 (52.26%) <= 16: 275 (47.74%)
HASHES, SIZE OF FIELDS
======================
17: 301 (52.26%) 22: 179 (31.08%) 13: 95 (16.49%)
12: 1 (0.17%)
Average: 17.89 Standard Deviation: 3.11
Min: 12 Max: 22
Powers of two distribution: (NOTE <= p means: p/2 < x <= p)
<= 32: 480 (83.33%) <= 16: 96 (16.67%)
HASHES, SIZE OF VALUES
======================
13: 116 (20.14%) 3: 103 (17.88%) 410: 44 (7.64%)
409: 38 (6.60%) 408: 27 (4.69%) 14: 22 (3.82%)
407: 17 (2.95%) 395: 12 (2.08%) 406: 12 (2.08%)
392: 11 (1.91%) 396: 11 (1.91%) 393: 11 (1.91%)
4: 10 (1.74%) 12: 10 (1.74%) 411: 9 (1.56%)
5: 7 (1.22%) 376: 7 (1.22%) 405: 6 (1.04%)
349: 5 (0.87%) 347: 5 (0.87%) 359: 5 (0.87%)
(suppressed 43 items with perc < 0.5% for a total of 15.28%)
Average: 207.90 Standard Deviation: 193.12
Min: 1 Max: 416
Powers of two distribution: (NOTE <= p means: p/2 < x <= p)
<= 512: 298 (51.74%) <= 16: 151 (26.22%) <= 4: 113 (19.62%)
<= 8: 10 (1.74%) <= 2: 3 (0.52%) <= 1: 1 (0.17%)
redis-sampler's People
Contributors
Watchers
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.