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https://github.com/mongoeye/mongoeye
Schema and data analyzer for MongoDB written in Go.
https://github.com/mongoeye/mongoeye
aggregation analysis histogram mongodb nosql schema statistics visualization
Last synced: about 2 months ago
JSON representation
Schema and data analyzer for MongoDB written in Go.
- Host: GitHub
- URL: https://github.com/mongoeye/mongoeye
- Owner: mongoeye
- License: gpl-3.0
- Created: 2017-04-10T14:57:17.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-05-16T17:39:13.000Z (over 2 years ago)
- Last Synced: 2024-06-20T14:20:00.333Z (3 months ago)
- Topics: aggregation, analysis, histogram, mongodb, nosql, schema, statistics, visualization
- Language: Go
- Homepage:
- Size: 37.8 MB
- Stars: 160
- Watchers: 2
- Forks: 9
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-mongodb - Mongoeye - Schema and data analyzer: explore data in your collections (Tools / Development)
- awesome-mongodb - Mongoeye - Schema and data analyzer: explore data in your collections (Tools / Development)
- awesome-ccamel - mongoeye/mongoeye - Schema and data analyzer for MongoDB written in Go. (Go)
README
Schema and data analyzer for [MongoDB](https://www.mongodb.com) written in [Go](https://golang.org).
[](https://godoc.org/github.com/mongoeye/mongoeye)
[](https://coveralls.io/github/mongoeye/mongoeye?branch=master)
[](https://travis-ci.com/mongoeye/mongoeye)
[](https://goreportcard.com/report/github.com/mongoeye/mongoeye)
## Overview
Mongoeye provides a quick overview of the data in your MongoDB database.
### Key features
* *Fast:* [the fastest](https://github.com/mongoeye/mongoeye/blob/master/_misc/comparison.png) schema analyzer for MongoDB
* *Single binary:* pre-built [binaries](https://github.com/mongoeye/mongoeye/releases) for Windows, Linux, and MacOS (Darwin)
* *Local analysis:* quick local analysis using a parallel algorithm (MongoDB 2.0+)
* *Remote analysis:* distributed analysis in database using the aggregation framework (MongoDB 3.5.10+)
* *Rich features:* [histogram](#value-histogram) (value, length, weekday, hour), [most frequent values](#frequency-of-values), ...
* *Integrable:* [table](#table-output), [JSON or YAML output](#json-and-yaml-output)
## Demo
## Table of Contents
* [Installation](#installation)
* [Compilation](#compilation)
* [Usage](#usage)
* [Table output](#table-output)
* [JSON and YAML output](#json-and-yaml-output)
* [Features](#features)
* [Value - min, max, avg](#value---min-max-avg)
* [Length - min, max, avg](#length---min-max-avg)
* [Number of unique values](#number-of-unique-values)
* [Frequency of values](#frequency-of-values)
* [Value histogram](#value-histogram)
* [Length histogram](#length-histogram)
* [Weekday histogram](#weekday-histogram)
* [Hour histogram](#hour-histogram)
* [Scope of analysis](#scope-of-analysis)
* [List of flags and options](#list-of-flags-and-options)
* [License](#license)
## Installation
Mongoeye is one executable binary file.
You can download the archive from [GitHub releases page](https://github.com/mongoeye/mongoeye/releases) and extract the binary file for your platform.
## Compilation
It is required to have [Go 1.8](https://golang.org). All external dependencies are part of the repository in the [vendor](https://github.com/mongoeye/mongoeye/tree/master/vendor) directory.
Compilation process:
```
$ go get github.com/mongoeye/mongoeye
$ cd $GOPATH/src/github.com/mongoeye/mongoeye
$ make build
```
For development, you need additional dependencies that can be installed using `make get-deps`.
The test architecture uses the [Docker](https://www.docker.com) to create the testing MongoDB database.
If you want to contribute to this project, see the actions in [Makefile](https://github.com/mongoeye/mongoeye/blob/master/Makefile) and the [_contrib](https://github.com/mongoeye/mongoeye/tree/master/_contrib) directory.
## Usage
```
mongoeye [host] database collection [flags]
```
The command `mongoeye --help` lists all available options.
### Table output
Default output format is table. It shows only schema without other analyzes.
Example table output:
```
KEY │ COUNT │ %
────────────────────────────────────────────
all documents │ 2548 │
analyzed documents │ 1000 │ 39.2
│ │
_id - objectId │ 1000 │ 100.0
address │ 1000 │ 100.0
│ - int │ 1 │ 0.1
└╴- string │ 999 │ 99.9
address line 2 - string │ 1000 │ 100.0
name - string │ 1000 │ 100.0
outcode - string │ 1000 │ 100.0
postcode - string │ 1000 │ 100.0
rating │ 1000 │ 100.0
│ - int │ 523 │ 52.3
│ - double │ 451 │ 45.1
└╴- string │ 26 │ 2.6
type_of_food - string │ 1000 │ 100.0
URL - string │ 1000 │ 100.0
OK 0.190s (local analysis)
1000/2548 docs (39.2%)
9 fields, depth 2
```
### JSON and YAML output
Use `--format json` or `--format yaml` flags to set these formats.
For output to a file use the option `-F /path/to/file`.
## Features
This chapter explains the features of Mongoeye and their various outputs.
Use `--format json` or `--format yaml` to get detailed results, otherwise only the schema table will appear.
The output of the analysis always contains these basic keys:
* **database**: database name
* **collection**: collection name
* **plan**: `local` for local analysis, `db` for analysis using aggregation framework
* **duration**: duration of analysis
* **allDocs**: number of all documents in collection
* **analyzedDocs**: number of analyzed documents from collection
* **fieldsCount**: number of found fields
* **fields**: result of the analysis for each field
* **name**: name of field
* **level**: level of nested field, `0` is root level
* **count**: number of occurrences
* **types**: result of the analysis for each type of field
* **type**: name of type
* **count**: number of occurrences of type
**Example result:**
```yaml
database: company
collection: users
plan: local
duration: 46.515331ms
allDocs: 2548
analyzedDocs: 1000
fieldsCount: 9
fields:
- name: rating
level: 0
count: 1000
types:
- type: int
count: 549
< other outputs according to settings >
```
### Value - min, max, avg
Use the flag `--value` or `-v` to enable calculation of minimum, maximum, and average values.
**Supported types**:
* Minimum and maximum: `objectId`, `double`, `string`, `bool`, `date`, `int`, `timestamp`, `long`, `decimal`
* Average: `double`, `bool`, `int`, `long`, `decimal`
**Example result:**
```yaml
value:
min: 11.565586
max: 60.206787
avg: 38.51128
```
### Length - min, max, avg
Use the flag `--length` or `-l` to enable calculation of minimum, maximum, and average lengths.
**Supported types**: `string`, `array`, `object`
**Example result:**
```yaml
length:
min: 29
max: 153
avg: 112
```
### Number of unique values
Use the flag `--count-unique` to count all unique values.
**Supported types**: `double`, `string`, `date`, `int`, `timestamp`, `long`, `decimal`
**Example result:**
```yaml
unique: 894
```
### Frequency of values
Use the flag `--most-freq N` or `--least-freq N` to get the most or least occurring values.
**Supported types**: `double`, `string`, `date`, `int`, `timestamp`, `long`, `decimal`
**Example result:**
```yaml
mostFrequent:
- value: USD
count: 599
- value: EUR
count: 21
- value: GBP
count: 5
- value: CAD
count: 4
leastFrequent:
- value: EUR
count: 21
- value: GBP
count: 5
- value: CAD
count: 4
- value: JPY
count: 3
```
### Value histogram
Use the flag `--value-hist` or `-V` to generate value histogram.
**Supported types**: `objectId` *- processed as a date*, `double`, `date`, `int`, `long`, `decimal`
#### Calculation of step
Flag `--value-hist-steps` sets the maximum number of steps (default `100`).
* Step of the `int` and `long` type is a whole number
* Step of the `double` and `decimal` type is:
* the smallest possible multiplication of [`1`, `5` or `2.5`] and `10^n` so the max. number of steps is kept
* eg. ..., `100`, `50`, `25`, `10`, `5`, `2.5`, `1`, `0.5`, `0.25`, `0.1`, ...
* Step of the `date` and `objectId` type is rounded to:
* 1, 2, 5, 10, 15, 30 `seconds`
* 1, 2, 5, 10, 15, 30 `minutes`
* 1, 2, 3, 6, 12 `hours`
* 1, 2, 3, 4, ... `days`
**Example result:**
```yaml
valueHistogram:
start: 2.5
end: 12
range: 9.5
step: 0.5
numOfSteps: 19
intervals: [36, 25, 14, 81, 95, 86, 59, 6, 82, 84, 62, 33, 19, 9, 1, 14, 67, 2, 45]
```
**Graphic representation:**
### Length histogram
Use the flag `--length-hist` or `-L` to generate length histogram.
Flag `--length-hist-steps` sets the maximum number of steps (default `100`).
**Supported types**: `string`, `array`, `object`
**Example result:**
```yaml
lengthHistogram:
start: 0
end: 300
range: 300
step: 50
numOfSteps: 6
intervals: [96, 78, 3, 1, 1, 0]
```
### Weekday histogram
Use the flag `--weekday-hist` or `-W` to generate weekday histogram.
To determine the day of week it uses the time zone from the `--timezone` flag (default `local`).
First day is `Sunday`.
**Example result:**
```yaml
weekdayHistogram: [5, 48, 23, 124, 45, 15, 87]
```
### Hour histogram
Use the flag `--hour-hist` or `-H` to generate weekday histogram.
To determine the hour it uses the time zone from the `--timezone` flag (default `local`).
First value is for interval `[ 00, 01 )`, last for interval `[ 23, 24 )`.
**Example result:**
```yaml
hourHistogram: [47, 73, 18, 26, 30, 46, 91, 13, 28, 11, 52, 99, 76, 25, 94, 51, 87, 86, 19, 22, 11, 62, 28, 47]
```
## Scope of analysis
*The scope of analysis is defined by the following options.*
The **`--match`** option is applied as the first:
- it selects documents for the analysis using [$match aggregation](https://docs.mongodb.com/manual/reference/operator/aggregation/match/)
- value is a string in JSON format
- suitable for include/exclude documents from analysis
- by default, all documents are included (if the argument is not present)
The **`--sample`** option is applied as the second:
- determines the sampling method using [$sort](https://docs.mongodb.com/manual/reference/operator/aggregation/sort/), [$limit](https://docs.mongodb.com/manual/reference/operator/aggregation/limit/) and [$sample](https://docs.mongodb.com/manual/reference/operator/aggregation/sample/) aggregations
- valid values are: `all`, `first:N`, `last:N`, `random:N`, where `N > 1`
- default value is `random:1000`
The **`--project`** option is applied as the third:
- before the analysis it modifies document using [$project aggregation](https://docs.mongodb.com/manual/reference/operator/aggregation/project/)
- value is a string in JSON format
- suitable for include/exclude fields from analysis
- default is not applied (if the argument is not present)
***Note:** Be sure to escape JSON options correctly, eg. `--project "{\"Field\": 0}"`.*
## List of flags and options
#### Connection options
```
--host mongodb host (default "localhost:27017")
--connection-mode connection mode (default "SecondaryPreferred")
--connection-timeout connection timeout (default 5)
--socket-timeout socket timeout (default 300)
--sync-timeout sync timeout (default 300)
```
#### Authentication
```
-u, --user username for authentication (default "admin")
-p, --password password for authentication
--auth-db auth database (default: same as the working db)
--auth-mech auth mechanism
```
#### Input options
```
--db database for analysis
--col collection for analysis
--match filter documents before analysis (json, $match aggregation)
-s, --sample all, first:N, last:N, random:N (default "random:1000")
--project filter/project fields before analysis (json, $project aggregation)
-d, --depth max depth in nested documents (default 2)
```
#### Output options
```
--full all available analyzes
-v, --value get min, max, avg value
-l, --length get min, max, avg length
-V, --value-hist get value histogram
--value-hist-steps max steps of value histogram >=3 (default 100)
-L, --length-hist get length histogram
--length-hist-steps max steps of length histogram >=3 (default 100)
-W, --weekday-hist get weekday histogram for dates
-H, --hour-hist get hour histogram for dates
--count-unique get count of unique values
--most-freq get the N most frequent values
--least-freq get the N least frequent values
-f, --format output format: table, json, yaml (default "table")
-F, --file path to the output file
```
#### Other options
```
-t, --timezone timezone, eg. UTC, Europe/Berlin (default "local")
--use-aggregation analyze with aggregation framework (mongodb 3.5.10+)
--string-max-length max string length (default 100)
--array-max-length analyze only first N array elements (default 20)
--concurrency number of local processes (default 0 = auto)
--buffer size of the buffer between local stages (default 5000)
--batch size of batch from database (default 500)
--no-color disable color output
--version show version
-h, --help show this help
```
### Environment variables
Environment variables can also be used for configuration.
The names of the environment variables have the `MONGOEYE_` prefix and match the flags.
Instead of the `--count-unique` flag, for example, you can use `export MONGOEYE_COUNT-UNIQUE=true`.
## TODO
* Create a shared library for integration into other languages (Python, Node.js, ...)
* Selection of fields for analysis (include and exclude list)
* TLS/SSL support
* Create a [web interface](https://github.com/mongoeye/mongoeye-ui).
## Donation
If is this tool useful to you, so feel free to support its further development.
[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=JEMPF6RQJP7XA)
## License
Mongoeye is under the GPL-3.0 license. See the [LICENSE](LICENSE.md) file for details.
AMDG