You can access the command line in the Docker image to perform configuration and run MapD utilities.
You need to know the container-id to access the command line. Use the command below to list the running containers.
docker container ls
You will receive output similar to the following.
||"/bin/sh -c '/mapd..."
||3 days ago
||Up 3 days
Access the command line in your Docker image using the following command.
docker exec -it <container-id> bash
Where <container-id> is the container in which MapD is running.
To verify that all systems are go, load some sample data, perform a mapdql query, and generate a pointmap using Immerse.
MapD ships with two sample datasets of airline flight information collected in 2008. To install the sample data, run the following command from the Docker image command line.
When prompted, choose whether to insert dataset 1 (7 million rows) or dataset 2 (10 thousand rows). The examples below use the smaller 10 thousand row dataset.
Enter dataset number to download, or 'q' to quit:
Connect to MapD Core by entering the following command (default password is HyperInteractive):
Enter a SQL query such as the following:
mapdql> SELECT origin_city AS "Origin", dest_city AS "Destination", AVG(airtime) AS "Average Airtime" FROM flights_2008_10k WHERE distance < 175 GROUP BY origin_city, dest_city;
The results should be similar to the results below.
Connect to Immerse using a web browser connected to your host machine on port 9092. For example, http://localhost:9092.
Create a new dashboard and a pointmap to verify that backend rendering is working.
- Click New Dashboard.
- Select the flights_2008_10K table as the datasource.
- Click Connect to Table.
- Click Add Chart.
- Click SCATTER.
- Click X Axis +Add Measure.
- Choose arrdelay.
- Click Y Axis +Add Measure.
- Choose depdelay.
The resulting chart shows, unsurprisingly, that there is a correlation between departure delay and arrival delay.