Developer’s guide

This section provides both an overview and detailed information abouth BioThings Studio, and is specifically aimed to developers who like to know more about internals.

A complementary tutorial is also available, explaining how to setup and use BioThings Studio, step-by-step, by building an API from a flat file.

What is BioThings Studio

BioThings Studio is a pre-configured, ready-to-use application. At its core is BioThings Hub, the backend service behind all BioThings APIs.

BioThings Hub: the backend service

Hub is responsible for maintaining data up-to-date, and creating data releases for the BioThings frontend.

The process of integrating data and creating releases involves different steps, as shown in the following diagram:

../_images/hubarch.png
  • data is first downloaded locally using dumpers

  • parsers will then convert data into JSON documents, those will be stored in a Mongo database using uploaders

  • when using multiple sources, data can be combined together using mergers

  • data releases are then created by either indexing data to an ElasticSearch cluster with indexers, or by computing the differences between the current release and previous one, using differs, and applying these differences using syncers

The final index along with the Tornado application represents the frontend that is actually queried by the different available clients, and is out of this document’s scope.

BioThings Studio

The architecture and different software involved in this system can be quite intimidating. To help the whole service is packaged as a pre-configured application, BioThings Studio. A docker image is available Docker Hub registry, and be pulled using:

$ docker pull biothings/biothings-studio:0.2a
../_images/hubstack.png

A BioThings Studio instance expose several services on different ports:

  • 8080: BioThings Studio web application port

  • 7022: BioThings Hub SSH port

  • 7080: BioThings Hub REST API port

  • 9200: ElasticSearch port

  • 27017: MongoDB port

  • 8000: BioThings API, once created, it can be any non-priviledged (>1024) port

  • 9000: Cerebro, a webapp used to easily interact with ElasticSearch clusters

BioThings Hub and the whole backend service can be accessed through different options according to some of these services

  • a web application allows interaction with the most used elements of the service (port 8080)

  • a console, accessible through SSH, gives access to more commands, for advanced usage (port 7022)

  • a REST API and a websocket (port 7080) can be used to interact with the Hub, query the differents objects inside, and get real-time notifications when processes are running. This interface is a good choice for third-party integration.

Who should use BioThings Studio ?

BioThings Studio can be used in different scenarios:

  • you want to contribute to an existing BioThings API by integrating a new data source

  • you want to run your own BioThings API but don’t want to have to install all the dependencies and learn how to configure all the sub-systems

Filesystem overview

Several locations on the filesystem are important to notice, when it comes to change default configuration or troubleshoot the application.

  • Hub (backend service) is running under biothings user, running code is located in /home/biothings/biothings_studio. It heavely relies on BioThings SDK located in /home/biothings/biothings.api.

  • Several scripts/helpers can be found in /home/biothings/bin:

    • run_studio is used to run the Hub in a tmux session. If a session is already running, it will first kill it and create new one. We don’t have to run this manually when the studio first starts, it is part of the starting sequence.

    • update_studio is used to fetch the latest code for BioThings Studio

    • update_biotnings, same as above but for BioThings SDK

  • /data contains several important folders:

    • mongodb folder, where MongoDB server stores its data

    • elasticsearch folder, where ElasticSearch stores its data

    • biothings_studio folder, containing different sub-folders used by the Hub

      • datasources contains data downloaded by the different dumpers, it contains sub-folders named according to the datasource’s name. Inside the datasource folder can be found the different releases, one per folder.

      • dataupload is where data is stored when uploading data to the Hub (see below dedicated section for more).

      • logs contains all log files produced by the Hub

      • plugins is where data plugins can be found (one sub-folder per plugin’s name)

Note

Instance will store MongoDB data in /data/mongodb, ElasticSearch data in /data/elasticsearch/ directory, and downloaded data and logs in /data/biothings_studio. Those locations could require extra disk space, if needed Docker option -v can be used to mount a directory from the host, inside the container. Please refer to Docker documentation. It’s also important to give enough permissions so the differences services (MongoDB, ElasticSearch, NGNIX, BioThings Hub, …) can actually write data on the docker host.

Configuration files

BioThings Hub expects some configuration variables to be defined first, in order to properly work. In most BioThings Studio, a config_hub.py defines those parameters, either by providing default value(s), or by setting it as ConfigurationError exception. In that latter case, it means no defaults can be used and user/developer has to define it. A final config.py file must be defined, it usually imports all parameters from config_hub.py (from config_hub import *). That config.py has to be defined before the Hub can run.

Note

This process is only required when implementing or initializing a Hub from scratch. All BioThings Studio applications comes with that file defined, the Hub is ready to be used.

It’s also possible to override parameters directly from the webapp/UI. In that case, new parameters’ values are stored in the internal Hub database. Upon start, Hub will check that database and supersed any values that are defined directly in the python configuration files. This process is handled by class biothings.ConfigurationManager.

Finally, a special (simple) dialect can be used while defining configuration parameters, using special markup within comments above declaration. This allows to: * provide documentation for parameters * put parameters under different categories * mark a parameter as read-only * set a parameter as “invisible” (not exposed)

This process is used to expose Hub configuration through the UI, automatically providing documentation in the webapp without having to duplicate code, parameters and documentation. For more information, see class biothings.ConfigurationParser, as well as existing configuration files in the different studios.

Services check

Let’s enter the container to check everything is running fine. Services may take a while, up to 1 min, before fully started. If some services are missing, the troubleshooting section may help.

$ docker exec -ti studio /bin/bash

root@301e6a6419b9:/tmp# netstat -tnlp
Active Internet connections (only servers)
Proto Recv-Q Send-Q Local Address           Foreign Address         State       PID/Program name
tcp        0      0 0.0.0.0:7080            0.0.0.0:*               LISTEN      -
tcp        0      0 0.0.0.0:9000            0.0.0.0:*               LISTEN      -
tcp        0      0 127.0.0.1:27017         0.0.0.0:*               LISTEN      -
tcp        0      0 0.0.0.0:7022            0.0.0.0:*               LISTEN      -
tcp        0      0 0.0.0.0:9200            0.0.0.0:*               LISTEN      -
tcp        0      0 0.0.0.0:8080            0.0.0.0:*               LISTEN      166/nginx: master p
tcp        0      0 0.0.0.0:9300            0.0.0.0:*               LISTEN      -
tcp        0      0 0.0.0.0:22              0.0.0.0:*               LISTEN      416/sshd
tcp6       0      0 :::7080                 :::*                    LISTEN      -
tcp6       0      0 :::7022                 :::*                    LISTEN      -
tcp6       0      0 :::22                   :::*                    LISTEN      416/sshd

Specifically, BioThings Studio services’ ports are: 7080, 7022 and 8080.

Overview of BioThings Studio web application

BioThings Studio web application can simply be accessed using any browser pointing to port 8080. The home page shows a summary of current data recent updates. For now, it’s pretty quiet since we didn’t integrate any data yet.

../_images/homeempty.png

Let’s have a quick overview of the different elements accessible through the webapp. On the top left is the connection widget. By default, BioThings Studio webapp will connect to the hub API through port 7080, the one running with docker. But the webapp is a static web page so you can access any other Hub API by configuring a new connection:

../_images/connectionlist.png

Enter the Hub API URL, http://<host>:<port> (you can omit http://, the webapp will use that scheme by default)

../_images/connectioncreate.png

The new connection is now listed and can be accessed quickly later simply by selecting it. Note the connection can be deleted with the “trash” icon, but cannot be edited.

../_images/connectionlist2.png

Following are several tabs giving access to the main steps involved in building a BioThings API. We’ll get into those in more details while we create our new API. On the right, we have different information about jobs and resources:

../_images/commands.png

Running commands are show in this popup, as well as as commands that have been running before, when switching to “Show all”

../_images/processes.png

When jobs are using parallelization, processes will show information about what is running and how much resources each process takes. Notice we only have 1 process available, as we’re running a t2.medium instance which only has 2 CPU, Hub has automatically assigned half of them.

../_images/threads.png

BioThings Hub also uses threads for parallelization, their activity will be show here. Number of queued jobs, waiting for a free process or thread, is showned, as well as the total amount of memory the Hub is currenly using

../_images/notifs.png

In this popup are shown all notifications coming from the Hub, in real-time, allowing to follow all jobs and activity.

../_images/loader.png

A first circle shows the page loading activity. Gray means nothing active, flashing blue means webapp is loading information from the Hub, and red means an error occured (error should be found in either notifications or by openin the logs from the bottom right corner).

The next button with a cog icon gives access to the configuration and is described in the next section.

../_images/websocket.png

Finally, a logo shows the websocket connection status (green power button means “connected”, red plug means “not connected”) on average)

Configuration

By clicking on the cog icon in the bar on the right, Hub configuration can be accessed. The configuration parameters, documentation, sections are defined in python configuration files (see Configuration files). Specifically, if a parameter is hidden, redacted or/and read-only, it’s because of how it was defined in the python configuration files.

../_images/configform.png

Any parameter must be entered in a JSON format. Ex: double quotes for strings, square brackets to define lists, etc… Once a parameter has been changed, change can be saved using the “Save” button, available for each parameters. The “Reset” button can be used to switch back the original, default value that was defined in the configuration files.

Ex: Update Hub’s name

../_images/edithubname.png

First enter the new name, for paramerer HUB_NAME. Because the value has changed, the “Save” button is available.

../_images/savehubname.png

Upon validation, a green check mark is shown, and because the value is not the default one, the “Reset” button is now available. Clicking on it will switch back the value for that parameter to the original, default one.

../_images/resethubname.png

Note each time a parameter is changed, Hub needs to be restarted, as shown on the top.

../_images/needrestart.png

Data plugin architecture and specifications

BioThings Studio allows to easily define and register datasources using data plugins. As of BioThings Studio 0.2b, there are two different types of data plugin.

1. Manifest-based data plugins

  • a manifest.json file

  • other python files supporting the declaration in the manifest.

The plugin name, that is, the folder name containing the manifest file, gives the name to the resulting datasource.

A manifest file is defined like this:

{
    "version": "0.2",
    "__metadata__" : { # optional
        "url" : "<datasource website/url>",
        "license_url" : "<url>",
        "licence" : "<license name>",
    "author" : {
        "name" : "<author name>",
        "url" : "<link to github's author for instance>"
    }
    },
    "requires" : ["lib==1.3","anotherlib"],
    "dumper" : {
        "data_url" : "<url>" # (or list of url: ["<url1>", "<url1>"]),
        "uncompress" : true|false, # optional, default to false
        "release" : "<path.to.module>:<function_name>"  # optional
        "schedule" : "0 12 * * *"  # optional
    },
    "uploader" : { # optional, a manifest is allowed to only have a "dumper" section
        "parser" : "<path.to.module>:<function_name>",
        "on_duplicates" : "ignore|error|merge" # optional, default to "error"
    }
}

Note

it’s possible to only have a dumper section, without any uploader specified. In that case, the data plugin will only download data and won’t provide any way to parse and upload data.

  • a version defines the specification version the manifest is using. Currently, version 0.2 should be used. This is not the version of the datasource itself.

  • an optional (but highly recommended) __metadata__ key provides information about the datasource itself, such as a website, a link to its license, the license name. This information, when provided, are displayed in the /metadata endpoint of the resulting API.

  • a requires section, optional, describes dependencies that should be installed for the plugin to work. This uses pip behind the scene, and each element of that list is passed to pip install command line. If one dependency installation fails, the plugin is invalidated. Alternately, a single string can be passed, instead of a list.

  • a dumper section specifies how to download the actual data.

    • data_url specifies where to download the data from. It can be a URL (string) or a list of URLs (list of strings). Currently supported protocols are http(s) and ftp. URLs must point to individual files (no wildcards) and only one protocol is allowed within a list of URLs (no mix of URLs using htttp and ftp are allowed). All files are download in a data folder, determined by config.DATA_ARCHIVE_ROOT/<plugin_name>/<release>

    • uncompress: once downloaded, this flag, if set to true, will uncompress all supported archived found in the data folder. Currently supported format are: *.zip, *.gz, *.tar.gz (includes untar step)

    • schedule will trigger the scheduling of the dumper, so it automatically checks for new data on a regular basis. Format is the same as crontabs, with the addition of an optional sixth parameter for scheduling by the seconds.

      Ex: * * * * * */10 will trigger the dumper every 10 seconds (unless specific use case, this is not recommanded).

      For more information, Hub relies on aiocron for scheduling jobs.

    • release optionally specifies how to determine the release number/name of the datasource. By default, if not present, the release will be set using:

      • Last-Modified header for an HTTP-based URL. Format: YYYY-MM-DD

      • ETag header for an HTTP-based URL if Last-Modified isn’t present in headers. Format: the actual etag hash.

      • MDTM ftp command if URL is FTP-based.

      If a list of URLs is specified in data_url, the last URL is the one used to determine the release. If none of those are available, or not satisfactory, a release section can be specified, and should point to a python module and a function name following this format: module:function_name. Within this module, function has the following signature and should return the release, as a string. set_release is a reserver name and must not be used.

def function_name(self):
    # code
    return "..."

self refers to the actual dumper instance, that is, either a biothings.hub.dataload.dumper.HTTPDumper or a biothings.hub.dataload.dumper.FTPDumper depending on the protocol. All properties, methods from the instance are available, specifically:

  • self.client, the actual underlying client used to download files, which is either a request.Session or ftplib.FTP instance, and should be prefered over initializing a new connection/client.

  • self.SRC_URLS, containing the list of URLs (if only one URL was specified in data_url, this will be a list of one element), which is commonly used to inspect and possibly determine the release.

  • an uploader section specifies how to parse and store (upload):

    • parser key defined a module and a function name within that module. Format: module:function_name. Function has the following signature and return a list of dictionary

    (or yield dictionaries) containing at least a _id key reprensenting a unique identifier (string) for this document:

def function_name(data_folder):
    # code
    yield {"_id":"..."}

data_folder is the folder containing the previously downloaded (dumped) data, it is automatically set to the latest release available. Note the function doesn’t take an filename as input, it should select the file(s) to parse.

  • on_duplicates defines the strategy to use when duplicated record are found (according to the _id key):

    • error (default) will raise an exception if duplicates are found

    • ignore will skip any duplicates, only the first one found will be store

    • merge will merge existing document with the duplicated one. Refer to biothings.hub.dataload.storage.MergerStorage class for more.

  • parallelizer points to a module:function_name that can be used when the uploader can be parallelized. If multiple input files exist, using the exact same parser, the uploader can be parallelized using that option. The parser should take an input file as parameter, not a path to a folder. The parallizer function should return a list of tuples, where each tuple corresponds to the list of input parameters for the parser. jobs is a reserved name and must not be used.

  • mapping points to a module:classmethod_name that can be used to specify a custom ElasticSearch mapping. Class method must return a python dictionary with a valid mapping. get_mapping is a reserved name and must not be used. There’s no need to add @classmethod decorator, Hub will take care of it, the first and only argument is a class. Ex:

def custom_mapping(cls):
    return {
        "root_field": {
             "properties": {
                 "subfield": {
                     "type": "text",
                 }
             }
         }
    }

Note

Please see https://github.com/sirloon/mvcgi for a simple plugin definition. https://github.com/sirloon/gwascatalog will show how to use the release key and https://github.com/sirloon/FIRE will demonstrate the parallelization in the uploader section.

2. “Advanced” data plugins

This type of plugins is more advanced in the sense that it’s plain python code. They typically come from a code export of a manifest-based plugin. The resulting python code defines dumpers and uploaders as python class, inheriting from BioThings SDK components. These plugins can be written from scratch, they’re “advanced” because they require more knowledge about BioThings SDK.

In the root folder (local folder or remote git repository), a __init__.py is expected to be found, and should contains imports for one dumper, and one or more uploaders.

An example of advanced data plugin can found at https://github.com/sirloon/mvcgi_advanced.git. It’s coming from “mvcgi” manifest-based plugin, where code was exported.

Hooks and custom commands

While it’s possible to define custom commands for the Hub console, by deriving class biothings.hub.HubServer, there’s also an easy way to enrich existing commands using hooks. A hook is a python file located in HOOKS_FOLDER (defaulting to ./hooks/). When the Hub starts, it inspects this folder and “inject” hook’s namespace into its console. Everything available from within the hook file becomes available in the console. On the other hand, hook can use any commands available in the Hub console.

Hooks provides an easy way to “program” the Hub, based on existing commands. The following example defines a new command, which will archive any builds older than X days. Code can be found at https://github.com/sirloon/auto_archive_hook.git. File auto_archive.py should be copied into ./hooks/ folder. Upon restart, a new command named auto_archive is now part of the Hub. It’s also been scheduled automatically using schedule(...) command at the end of the hook.

The auto_archive function uses several existing Hub commands:

  • lsmerge: when given a build config name, returns a list of all existing build names.

  • archive: will delete underlying data but keep existing metadata for a given build name

  • bm.build_info: bm isn’t a command, but a shortcut for build_manager instance. From this instance, we can call build_info method which, given a build name, returns information about it, including the build_date field we’re interested in.

Note

Hub console is actually a python interpreter. When connecting to the Hub using SSH, the connection “lands” into that interpreter. That’s why it’s possible to inject python code into the console.

Note

Be careful. User-defined hooks can be conflicting with existing commands and may break the Hub. Ex: if a hook defines a command “dump”, it will replace, and potentially break existing one!