MySQL Config

First, let’s check the database configuration.

In a local development environment, you can run the mysql client like this:

make mysql

In a server environment, use the mysql client to connect to the database.

Next, check if alembic migrations have been run:

select * from alembic_version;
| version_num  |
| d2d9ecb12edc |
1 row in set (0.00 sec)

This needs to produce a single row and some version_num in it. If it isn’t there, check the Database Setup part of the deploy docs.

Now check the API keys:

select * from api_key\G
*************************** 1. row ***************************
                  valid_key: test
                     maxreq: NULL
             allow_fallback: 0
               allow_locate: 1
               allow_region: 1
             allow_transfer: 0
              fallback_name: NULL
               fallback_url: NULL
         fallback_ratelimit: NULL
fallback_ratelimit_interval: NULL
      fallback_cache_expire: NULL
        store_sample_locate: 100
        store_sample_submit: 100
1 row in set (0.00 sec)

And the export config:

select * from export_config;
| name     | batch | schema   | url  | skip_keys | skip_sources |
| internal |   100 | internal | NULL | NULL      | NULL         |
1 row in set (0.00 sec)

If you are missing either of these entries, then it’s likely you need to set up API keys and export configuration.


Check if the web service and celery containers can connect to the MySQL database and Redis datastore.

Follow the instructions in the Runtime Checks part of the the deploy docs. Make sure to call the /__heartbeat__ HTTP endpoint on the web application.

Another way to check connections is to start a container and try to connect to the two external connections from inside it.

In a local development environment, you can do this:

make shell

In a server environment, you need to run the container with configuration in the environment.

Once inside the container, you can do this:

$ redis-cli -u $REDIS_URI> keys *
1) "_kombu.binding.celery"
2) "unacked_mutex"
3) "_kombu.binding.celery.pidbox"

If the task worker containers are running or have been run at least once, you should see keys listed.

Similarly, we can connect to the MySQL database from inside the container. Using the same shell, you can run the mysql client:

Welcome to the MySQL monitor.  Commands end with ; or \g.

Substitute DBHOST, USERNAME, PASSWORD, and DBNAME according to your database setup.

Task Worker

The asynchronous task worker uses a Python framework called Celery. You can use the Celery monitoring guide for more detailed information.

A basic test is to call the inspect stats commands. Open a shell container and inside it run:

$ celery -A inspect stats
-> celery@388ec81273ba: OK
    "total": {
        "": 1,
        "": 1,
        "": 304,
        "": 66,
        "": 21,
        "": 29,
        "": 368

If you get Error: no nodes replied within time constraint., then Celery isn’t running.

If this section continues to be empty, something is wrong with the scheduler and it isn’t adding tasks to the worker queues.

Otherwise, the output is pretty long. Look at the “total” section. If you have your worker and scheduler container running for some minutes, this section should fill up with various tasks.

Data Pipeline

Now that all the building blocks are in place, let’s try to send real data to the service and see how it processes it.

Assuming containers for all three roles are running, we’ll use the HTTP geosubmit v2 API endpoint to send some new data to the service:

$ curl -H 'Content-Type: application/json' -d \
'{"items": [{"wifiAccessPoints": [{"macAddress": "94B40F010D01"}, {"macAddress": "94B40F010D00"}, {"macAddress": "94B40F010D03"}], "position": {"latitude": 51.0, "longitude": 10.0}}]}'

We can find this data again in Redis, open a Redis client and do:

lrange "queue_export_internal" 0 10
1) "{\"api_key\": \"test\", \"report\": {\"timestamp\": 1499267286717, \"bluetoothBeacons\": [], \"wifiAccessPoints\": [{\"macAddress\": \"94B40F010D01\"}, {\"macAddress\": \"94B40F010D00\"}, {\"macAddress\": \"94B40F010D03\"}], \"cellTowers\": [], \"position\": {\"latitude\": 51.0, \"longitude\": 10.0}}}"

The data pipeline is optimized for production use and processes data in batches or if data sits too long in a queue. We can use the later feature to trick the pipeline into processing data sooner.

In the same Redis client use:

expire "queue_export_internal" 300

This tells the queue to get deleted in 300 seconds. The scheduler runs a task to check this queue about once per minute and checks both its length and its remaining time-to-live.

If we check the available Redis keys again, we might see something like:

keys *
1) "_kombu.binding.celery"
2) "apiuser:submit:test:2017-07-05"
3) "update_wifi_0"
4) "unacked_mutex"
5) "statcounter_unique_wifi_20170705"
6) "_kombu.binding.celery.pidbox"

If we wait a bit longer, the update_wifi_0 entry should vanish.

Once that happened, we can check the database directly. On a MySQL client prompt do:

select hex(`mac`), lat, lon from wifi_shard_0;
| hex(`mac`)   | lat  | lon  |
| 94B40F010D00 |   51 |   10 |
| 94B40F010D01 |   51 |   10 |
| 94B40F010D03 |   51 |   10 |
3 rows in set (0.00 sec)

Once the data has been processed, we can try the public HTTP API again and see if we can locate us. To do that we can use both the geolocate and region APIs:

curl -H 'Content-Type: application/json' -d \
'{"wifiAccessPoints": [{"macAddress": "94B40F010D01"}, {"macAddress": "94B40F010D00"}, {"macAddress": "94B40F010D03"}]}'

This should produce a response:

{"location": {"lat": 51.0, "lng": 10.0}, "accuracy": 10.0}

And again using the region API:

curl -H 'Content-Type: application/json' -d \
'{"wifiAccessPoints": [{"macAddress": "94B40F010D01"}, {"macAddress": "94B40F010D00"}, {"macAddress": "94B40F010D03"}]}'
{"country_code": "DE", "country_name": "Germany"}

If you check Redis queues again, there’s a new entry in there for the geolocate query we just submitted:> lrange "queue_export_internal" 0 10
1) "{\"api_key\": \"test\", \"report\": {\"wifiAccessPoints\": [{\"macAddress\": \"94b40f010d01\"}, {\"macAddress\": \"94b40f010d00\"}, {\"macAddress\": \"94b40f010d03\"}], \"fallbacks\": {\"ipf\": true, \"lacf\": true}, \"position\": {\"latitude\": 51.0, \"longitude\": 10.0, \"accuracy\": 10.0, \"source\": \"query\"}}}"

Note the "source": "query" part at the end, which tells the pipeline the position data does not represent a GPS verified position, but was the result of a query.

You can use the same expire trick as above again, to get the data processed faster.

In the mysql client, you can see the result:

select hex(`mac`), last_seen from wifi_shard_0;
| hex(`mac`)   | last_seen  |
| 94B40F010D00 | 2017-07-05 |
| 94B40F010D01 | 2017-07-05 |
| 94B40F010D03 | 2017-07-05 |
3 rows in set (0.00 sec)

Since all the WiFi networks were already known, their position just got confirmed. This gets stored in the last_seen column, which tracks when the network was last confirmed in a query.