In this codelab, you'll get introduced to using Cloud Bigtable with the Java HBase client.

You'll learn how to

When you're done, you'll have several maps that show New York City bus data. For example, you'll create this heatmap of bus trips in Manhattan:

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You'll be looking at a dataset about New York City buses. There are more than 300 bus routes and 5,800 vehicles following those routes. Our dataset is a log that includes destination name, vehicle id, latitude, longitude, expected arrival time, and scheduled arrival time. The dataset is made up of snapshots taken around every 10 minutes for June 2017.

To get the best performance from Cloud Bigtable, you have to be thoughtful when you design your schema. Data in Cloud Bigtable is automatically sorted lexicographically, so if you design your schema well, querying for related data is very efficient. Cloud Bigtable allows for queries using point lookups by row key or row-range scans that return a contiguous set of rows. However, if your schema isn't well thought out, you might find yourself piecing together multiple row lookups, or worse, doing full table scans, which are extremely slow operations.

Plan out the queries

Our data has a variety of information, but for this codelab, you will use the location and destination of the bus.

With that information, you could perform these queries:

This set of queries can't all be done together optimally. For example, if you are sorting by time, you can't do a scan based on a location without doing a full table scan. You need to prioritize based on the queries you most commonly run.

For this codelab, you'll focus on optimizing and executing the following set of queries:

Design the row key

For this codelab, you will be working with a static dataset, but you will design a schema for scalability. You'll design a schema that allows you to stream more bus data into the table and still perform well.

Here is the proposed schema for the row key:

[Bus company/Bus line/Timestamp rounded down to the hour/Vehicle ID]. Each row has an hour of data, and each cell holds multiple time-stamped versions of the data.

For this codelab, you will use one column family to keep things simple. Here is an example view of what the data looks like. The data is sorted by row key.

Row key

cf:VehicleLocation.Latitude

cf:VehicleLocation.Longitude

...

MTA/M86-SBS/

1496275200000/NYCT_5824

40.781212 @20:52:54.00

40.776163 @20:43:19.00

40.778714 @20:33:46.00

-73.961942 @20:52:54.00

-73.946949 @20:43:19.00

-73.953731 @20:33:46.00

MTA/M86-SBS/

1496275200000/NYCT_5840

40.780664 @20:13:51.00

40.788416 @20:03:40.00

-73.958357 @20:13:51.00

-73.976748 @20:03:40.00

...

MTA/M86-SBS/

1496275200000/NYCT_5867

40.780281 @20:51:45.00

40.779961 @20:43:15.00

40.788416 @20:33:44.00

-73.946890 @20:51:45.00

-73.959465 @20:43:15.00

-73.976748 @20:33:44.00

...

...

...

...

...

Next, you'll create a Cloud Bigtable table.

First, create a new project. Use the built-in Cloud Shell, which you can open by clicking the "Activate Cloud Shell" button in the upper-righthand corner.

Set the following environment variables to make copying and pasting the codelab commands easier:

INSTANCE_ID="bus-instance"
CLUSTER_ID="bus-cluster"
TABLE_ID="bus-data"
CLUSTER_NUM_NODES=3
CLUSTER_ZONE="us-central1-c"

Cloud Shell comes with the tools that you'll use in this codelab, the gcloud command-line tool, the cbt command-line interface, and Maven, already installed.

Enable the Cloud Bigtable APIs by running this command.

gcloud services enable bigtable.googleapis.com bigtableadmin.googleapis.com

Create an instance by running the following command:

gcloud bigtable instances create $INSTANCE_ID \
    --cluster=$CLUSTER_ID \
    --cluster-zone=$CLUSTER_ZONE \
    --cluster-num-nodes=$CLUSTER_NUM_NODES \
    --display-name=$INSTANCE_ID

After you create the instance, populate the cbt configuration file and then create a table and column family by running the following commands:

echo project = $GOOGLE_CLOUD_PROJECT > ~/.cbtrc
echo instance = $INSTANCE_ID >> ~/.cbtrc

cbt createtable $TABLE_ID
cbt createfamily $TABLE_ID cf

Import a set of sequence files for this codelab from gs://cloud-bigtable-public-datasets/bus-data with the following steps:

Enable the Cloud Dataflow API by running this command.

gcloud services enable dataflow.googleapis.com

Run the following commands to import the table.

NUM_WORKERS=$(expr 3 \* $CLUSTER_NUM_NODES)
gcloud beta dataflow jobs run import-bus-data-$(date +%s) \
--gcs-location gs://dataflow-templates/latest/GCS_SequenceFile_to_Cloud_Bigtable \
--num-workers=$NUM_WORKERS --max-workers=$NUM_WORKERS \
--parameters bigtableProject=$GOOGLE_CLOUD_PROJECT,bigtableInstanceId=$INSTANCE_ID,bigtableTableId=$TABLE_ID,sourcePattern=gs://cloud-bigtable-public-datasets/bus-data/*

Monitor the import

You can monitor the job in the Cloud Dataflow UI. Also, you can view the load on your Cloud Bigtable instance with its monitoring UI. It should take 5 minutes for the entire import.

git clone https://github.com/googlecodelabs/cbt-intro-java.git
cd cbt-intro-java

Change to Java 11 by running the following commands:

sudo update-java-alternatives -s java-1.11.0-openjdk-amd64 && export JAVA_HOME=/usr/lib/jvm/java-11-openjdk-amd64/

The first query you'll perform is a simple row lookup. You'll get the data for a bus on the M86-SBS line on June 1, 2017 from 12:00 am to 1:00 am. A vehicle with id NYCT_5824 is on the bus line then.

With that information, and knowing the schema design (Bus company/Bus line/Timestamp rounded down to the hour/Vehicle ID,) you can deduce that the row key is:

MTA/M86-SBS/1496275200000/NYCT_5824

BusQueries.java

private static final byte[] COLUMN_FAMILY_NAME = Bytes.toBytes("cf");
private static final byte[] LAT_COLUMN_NAME = Bytes.toBytes("VehicleLocation.Latitude");
private static final byte[] LONG_COLUMN_NAME = Bytes.toBytes("VehicleLocation.Longitude");

String rowKey = "MTA/M86-SBS/1496275200000/NYCT_5824";
Result getResult =
    table.get(
        new Get(Bytes.toBytes(rowKey))
            .addColumn(COLUMN_FAMILY_NAME, LAT_COLUMN_NAME)
            .addColumn(COLUMN_FAMILY_NAME, LONG_COLUMN_NAME));

The result should contain the most recent location of the bus within that hour. But you want to see all the locations, so set the maximum number of versions on the get request.

BusQueries.java

Result getResult =
    table.get(
        new Get(Bytes.toBytes(rowKey))
            .setMaxVersions(Integer.MAX_VALUE)
            .addColumn(COLUMN_FAMILY_NAME, LAT_COLUMN_NAME)
            .addColumn(COLUMN_FAMILY_NAME, LONG_COLUMN_NAME));

In the Cloud Shell, run the following command to get a list of latitudes and longitudes for that bus over the hour:

mvn package exec:java -Dbigtable.projectID=$GOOGLE_CLOUD_PROJECT \
-Dbigtable.instanceID=$INSTANCE_ID -Dbigtable.table=$TABLE_ID \
-Dquery=lookupVehicleInGivenHour

You can copy and paste the latitudes and longitudes into MapMaker App to visualize the results. After a few layers, it will tell you to create a free account. You can create an account or just delete the existing layers you have. This codelab includes a visualization for each step, if you just want to follow along. Here is the result for this first query:

Now, let's view all the data for the bus line for that hour. The scan code looks pretty similar to the get code. You give the scanner a starting position and then indicate you only want rows for the M86-SBS bus line within the hour denoted by the timestamp 1496275200000.

BusQueries.java

Scan scan;
scan = new Scan();
scan.setMaxVersions(Integer.MAX_VALUE)
    .addColumn(COLUMN_FAMILY_NAME, LAT_COLUMN_NAME)
    .addColumn(COLUMN_FAMILY_NAME, LONG_COLUMN_NAME)
    .withStartRow(Bytes.toBytes("MTA/M86-SBS/1496275200000"))
    .setRowPrefixFilter(Bytes.toBytes("MTA/M86-SBS/1496275200000"));
ResultScanner scanner = table.getScanner(scan);

Run the following command to get the results.

mvn package exec:java -Dbigtable.projectID=$GOOGLE_CLOUD_PROJECT \
-Dbigtable.instanceID=$INSTANCE_ID -Dbigtable.table=$TABLE_ID \
-Dquery=scanBusLineInGivenHour

The Map Maker app can display multiple lists at once, so you can see which of the buses are the vehicle from the first query you ran.

An interesting modification to this query is to view the entire month of data for the M86-SBS bus line, and this is very easy to do. Remove the timestamp from the start row and prefix filter to get the result.

BusQueries.java

scan.withStartRow(Bytes.toBytes("MTA/M86-SBS/"))
    .setRowPrefixFilter(Bytes.toBytes("MTA/M86-SBS/"));

// Optionally, reduce the results to receive one version per column
// since there are so many data points.
scan.setMaxVersions(1);

Run the following command to get the results. (There will be a long list of results.)

mvn package exec:java -Dbigtable.projectID=$GOOGLE_CLOUD_PROJECT \
-Dbigtable.instanceID=$INSTANCE_ID -Dbigtable.table=$TABLE_ID \
-Dquery=scanEntireBusLine

If you copy the results into MapMaker, you can view a heatmap of the bus route. The orange blobs indicate the stops, and the bright red blobs are the start and end of the route.

Next, you will filter on buses heading east and buses heading west, and create a separate heatmap for each.

BusQueries.java

Scan scan;
ResultScanner scanner;
scan = new Scan();
SingleColumnValueFilter valueFilter =
    new SingleColumnValueFilter(
        COLUMN_FAMILY_NAME,
        Bytes.toBytes("DestinationName"),
        CompareOp.EQUAL,
        Bytes.toBytes("Select Bus Service Yorkville East End AV"));

scan.setMaxVersions(1)
    .addColumn(COLUMN_FAMILY_NAME, LAT_COLUMN_NAME)
    .addColumn(COLUMN_FAMILY_NAME, LONG_COLUMN_NAME);
scan.withStartRow(Bytes.toBytes("MTA/M86-SBS/"))
    .setRowPrefixFilter(Bytes.toBytes("MTA/M86-SBS/"));
scan.setFilter(valueFilter);
scanner = table.getScanner(scan);

Run the following command to get the results for buses going east.

mvn package exec:java -Dbigtable.projectID=$GOOGLE_CLOUD_PROJECT \
-Dbigtable.instanceID=$INSTANCE_ID -Dbigtable.table=$TABLE_ID \
-Dquery=filterBusesGoingEast

To get the buses going west, change the string in the valueFilter:

BusQueries.java

SingleColumnValueFilter valueFilter =
    new SingleColumnValueFilter(
        COLUMN_FAMILY_NAME,
        Bytes.toBytes("DestinationName"),
        CompareOp.EQUAL,
        Bytes.toBytes("Select Bus Service Westside West End AV"));

Run the following command to get the results for buses going west.

mvn package exec:java -Dbigtable.projectID=$GOOGLE_CLOUD_PROJECT \
-Dbigtable.instanceID=$INSTANCE_ID -Dbigtable.table=$TABLE_ID \
-Dquery=filterBusesGoingWest

Buses heading east

Buses heading west

By comparing the two heatmaps, you can see the differences in the routes as well as notice differences in the pacing. One interpretation of the data is that on the route heading west, the buses are getting stopped more, especially when entering Central Park. And on the buses heading east, you don't really see many choke points.

For the final query, you'll address the case when you care about many bus lines in an area:

BusQueries.java

private static final String[] MANHATTAN_BUS_LINES = {"M1","M2","M3",...

Scan scan;
ResultScanner scanner;
List<RowRange> ranges = new ArrayList<>();
for (String busLine : MANHATTAN_BUS_LINES) {
  ranges.add(
      new RowRange(
          Bytes.toBytes("MTA/" + busLine + "/1496275200000"), true,
          Bytes.toBytes("MTA/" + busLine + "/1496275200001"), false));
}
Filter filter = new MultiRowRangeFilter(ranges);
scan = new Scan();
scan.setFilter(filter);
scan.setMaxVersions(Integer.MAX_VALUE)
    .addColumn(COLUMN_FAMILY_NAME, LAT_COLUMN_NAME)
    .addColumn(COLUMN_FAMILY_NAME, LONG_COLUMN_NAME);
scan.withStartRow(Bytes.toBytes("MTA/M")).setRowPrefixFilter(Bytes.toBytes("MTA/M"));
scanner = table.getScanner(scan);

Run the following command to get the results.

mvn package exec:java -Dbigtable.projectID=$GOOGLE_CLOUD_PROJECT \
-Dbigtable.instanceID=$INSTANCE_ID -Dbigtable.table=$TABLE_ID \
-Dquery=scanManhattanBusesInGivenHour

Clean up to avoid charges

To avoid incurring charges to your Google Cloud Platform account for the resources used in this codelab you should delete your instance.

gcloud bigtable instances delete $INSTANCE_ID

What we've covered

Next steps