Shulou(Shulou.com)06/02 Report--

1. Preface

This practice is all personal learning, I chose to install Superset under window, and embed the background system practice. Summarize the practice process, and share the practice results with you for your reference. If you have better ideas, you are welcome to leave a message.

two。 Environment ready 2.1 install Python

It is recommended that you install Python 3.4 or above. Python version 2.7 has various coding problems on windows. Https://www.python.org/downloads/release/python-350/

Download Windows x86-64 executable installer. You can use exe's installation package directly, and select and add to the environment variable during installation.

Check: run python-V and pip-V under CMD, respectively. If the command cannot be found, you need to add the installation directory of python to the path environment variable.

2.2 install virtualenv

This step is optional, skip to step 4 if you install it directly. Because Superset requires more components to be installed, it is best to use virtualenv to set up a separate python environment.

When developing Python applications, there is only one version of Python3 installed on the system. All third-party packages will be installed by pip into Python3's site-packages directory.

If we were to develop multiple applications at the same time, they would all share a single Python, which is Python 3 installed on the system. What if application A requires jinja 2.7and application B requires jinja 2.6?

In this case, each application may need to have its own "independent" Python runtime environment. Virtualenv is used to create an "isolated" Python environment for an application.

Installation commands:

Pip install virtualenv

2.3 use virtualenv

First create a d:\ pythonVir directory on disk D.

Then activate:

Cd d:\ pythonVirvirtualenv env// waits for initialization to complete. / / activate: env\ Scripts\ activate

The interface after activation is shown in the following figure. Note that there is a (env) mark on the left side of the command line input, so that our subsequent operations will take effect in env and will not affect the overall pyhton environment.

2.4 install VS2015

Some of the dependent libraries in Superset need to be compiled using microsoft visual C++ 2010.

According to the instructions, you can also install Visual C++ 2015 Build Tools:

Http://landinghub.visualstudio.com/visual-cpp-build-tools

2.5 install sasl

Here is a big pit, I installed superset directly before the installation has been unsuccessful, reported an error (sasl.h can not find).

The solution is to download the corresponding version through http://www.lfd.uci.edu/~gohlke/pythonlibs/#sasl

For example, if we install python version 3.6 and the system is 64-bit, download sasl-0.2.1-cp36-cp36m-win_amd64.whl.

In addition, the solution to the occurrence of "Failed building wheel for xxx" during installation is as follows:

Cause: the corresponding whl file is missing.

Solution: download and install the corresponding whl file.

For example, if "Failed building wheel for python_geohash" appears, download the corresponding python version of the python_geohash file.

I am using the Python3.6 version, then find the python_geohash-0.8.5-cp36-cp36m-win_amd64.whl file to download.

Installation method:

Pip install F:\ python_geohash-0.8.5-cp36-cp36m-win32.whl

3. Install Superset

1) after the front environment is ready, begin to install superset.

Pip install superset

Execute the successful interface:

2) create an administrator account:

Fabmanager create-admin-app superset

The interface for the execution process is as follows:

3) initialize the database (under windows, go to the Python installation directory (or the virtual directory of virtualEnv), lib\ site-packages\ superset\ bin)

Execute the command:

Python superset db upgrade

4) load examples (all subsequent operations need to be done under lib\ site-packages\ superset\ bin)

Python superset load_examples

5) initialize roles and permissions

Python superset init

6) start the service, port 8088, and use-p to change the port number.

Python superset runserver-d4. Configure the data source

Superset uses sqllite by default. The following databases are supported:

MySQL

Pip install mysqlclient

Mysql://

Postgres

Pip install psycopg2

Postgresql+psycopg2://

Presto

Pip install pyhive

Presto://

Oracle

Pip install cx_Oracle

Oracle://

Sqlite

By default, there is.

Sqlite://

Redshift

Pip install sqlalchemy-redshift

Postgresql+psycopg2://

MSSQL

Pip install pymssql

Mssql://

Impala

Pip install impyla

Impala://

SparkSQL

Pip install pyhive

Jdbc+hive://

Greenplum

Pip install psycopg2

Postgresql+psycopg2://

Athena

Pip install "PyAthenaJDBC > 1.0.9"

Awsathena+jdbc://

Vertica

Pip install sqlalchemy-vertica-python

Vertica+vertica_python://

ClickHouse

Pip install sqlalchemy-clickhouse

Clickhouse://

After installing the database using pip, you can configure the relevant data sources in the Web interface.

For the connection string format of the database, see:

Http://docs.sqlalchemy.org/en/rel_1_0/core/engines.html#database-urls

5. Log in for trial

After logging in to superset, we can configure our own local data source, query and display the data.

6. Embed background application 6.1 modify configuration file

Modify the config.py configuration file in superset to change PUBLIC_ROLE_LIKE_GAMMA to True.

The meaning of the note:

Grant the public role the same set of permissions as the GAMMA role.

If you want anonymous users to view it, you can set it here, and the authorization display for a specific dataset on the dashboard can also be set here.

6.2 remove the X-Frame-Options restriction

Avoid iframe cross-site access problems.

6.3 add database permissions all database permissions are added here

Where:

Z can explore on Superset to export the chart

^ can explore json on Superset is the export chart json

0 all database access on all_database_access access all database permissions, or you can set a single

6.4 iframe embedding

The effect is as follows:

7. Redirect Superset Chart URL

Why do you need to redirect? The main purpose here is to hide the superset chart link for the background application to prevent malicious use after being scanned. As long as the background application rewrites a request link with permission control and redirects to the superset chart link, it can prevent data from leaking out.

Background code:

Then, for the link address: / chart/getDemoDashboardUrl, permissions can be managed in the background.

8. Interactive implementation of dynamic parameter transfer

The above can embed the chart in superset into the background application system, but how can you pass the parameters? Now, I'll sort out the implementation process here and follow the experimental example to see how it works.

If you take a look at the link address provided by the superset chart, you can see that json has been passed as a parameter. As follows:

Form_data= {"datasource": "3__table", "viz_type": "line", "slice_id": 63, "granularity_sqla": "ds", "time_grain_sqla": null, "since": "100 years ago", "until": "now", "metrics": [{"aggregate": "SUM", "column": {"column_name": "num_california", "expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END"}, "expressionType": "SIMPLE" "label": "SUM (num_california)"}], "adhoc_filters": [{"expressionType": "SIMPLE", "subject": "gender", "operator": "=", "comparator": "boy", "clause": "WHERE", "sqlExpression": null, "fromFormData": true, "filterOptionName": "filter_gtzm93u9ocq_9sy5vd5ocfg"}, {"expressionType": "SIMPLE", "subject": "name", "operator": "LIKE", "comparator": "Aaron", "clause": "WHERE" "sqlExpression": null, "fromFormData": true, "filterOptionName": "filter_6cgdixdoh4_5wrgyuorwoa"}], "groupby": ["name"], "limit": "10", "timeseries_limit_metric": {"aggregate": "SUM", "column": {"column_name": "num_california", "expression": "CASE WHEN state = 'CA' THEN num ELSE 0 END"}, "expressionType": "SIMPLE", "label": "SUM (num_california)"}, "order_desc": true "contribution": false, "row_limit": 50000, "color_scheme": "bnbColors", "show_brush": "auto", "show_legend": true, "rich_tooltip": true, "show_markers": false, "line_interpolation": "linear", "x_axis_label": "auto", "x_ticks_layout": "auto", "x_axis_format": "smart_date", "x_axis_showminmax": false "y_axis_label": "," left_margin ":" auto "," y_axis_showminmax ": false," y_log_scale ": false," y_axis_format ":" .3s "," y_axis_bounds ": [null,null]," rolling_type ":" None "," time_compare ": []," num_period_compare ":"," period_ratio_type ":" growth "," resample_how ": null," resample_rule ": null "resample_fillmethod": null, "annotation_layers": [], "compare_lag": "10", "compare_suffix": "o10Y", "markup_type": "markdown", "metric": "sum__num", "where": "," url_params ": {}}

Among them, the parameter configuration marked × × is the parameter configuration of the filter condition, so it is extracted and modified accordingly in the code:

The modified interface is as follows:

Enter the criteria, name: Amy, gender selection: girl, click to query as follows:

9. Concluding remarks

The above practice is the process of pre-research superset visualization tools, the preparation of samples are very simple. If it is actually applied to the project, redesign a set of expandable and easy-to-use architecture according to the principle of appeal, and polish it into a configurable product tool. Leave a foreshadowing here, do not elaborate for the time being, if you have good ideas, welcome to leave a message to exchange.

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