Change Streaming

Stream updates to DynamoDB

In your web browser, open a new tab for and choose Lambda

Select Functions in the left pane

You will see the three Lambda functions that the lab deploys, named <stack name>-WiringFunction-<string>, <stack name>-LambdaFunctionForDDBStreams-<string>, and <stack name>-StreamingFunction-<string>. Click <stack name>-StreamingFunction-<string>

This function will generate random updates for the clicks and purchases fields, and send those updates to DynamoDB. These updates will in turn be shipped to Amazon ES through the LambdaFunctionForDDBStreams. If you examine the code, you’ll see that the function ignores its input and simply runs in a loop, exiting just before it times out

Click Test

Type an Event Name

Since the inputs don’t matter, you don’t have to change them. Click Create

Click Test. This will run for 5 minutes, streaming changes to Amazon ES via DynamoDB

Analyze the changes with Kibana

If you examine the code for the LambdaFunctionForDDBStreams, you’ll see that when data is modified in your DynamoDB table, the function sends both the update and a log of the changes to the clicks and purchases to a logs-<date\> index. You can use Kibana to visualize this information.

Return to your Kibana tab (or open a new one)

Click Management in the navigation pane

Click Index Patterns. You use index patterns to tell Kibana which indexes hold time-series data that you want to use for visualizations

In the Index Pattern text box, type logs (leave the ***** that Kibana adds automatically)

Kibana reports Success in identifying an index that matches that pattern

Click Next Step

Here you tell Kibana which field contains the time stamp for your records. Select @timestamp from the Time filter field name drop down

Click Create Index Pattern

Kibana recognizes the fields in your index and displays them for you

Click Discover in the navigation pane

This screen lets you view a traffic graph (the count of all events over time) as well as search your log lines for particular values


(Note, you can see my data covers 5 minutes and then stops. That’s because the Lambda function that’s streaming changes terminates. You can go back to the Lambda console and click Test again to stream more changes)

You can also use Kibana to build visualizations and gather them into a dashboard for monitoring events in near real time

Click Visualize in the navigation pane

Click Create a visualization You can see that Kibana has many different kinds of visualizations you can build

Click Line

On this screen, you tell Kibana which index pattern to use as the source for your visualization. Click logs*

When building Kibana visualizations you will commonly put time on the X-axis and a function of a numeric field on the Y-axis to graph a value over time

In the Buckets section, click X-Axis

In the Aggregation drop down, select Date Histogram

Click aggr to change the visualization. You now have a graph of time buckets on the X-Axis and the Count of events on the Y-Axis

In the Aggregation drop down for the Y-Axis, select Sum and in the Field drop down, select purchases to see the sum of all purchases, broken down by time

You can monitor changes in this metric, in near real time, by clicking sin Kibana’s top menu bar, and choosing 10 seconds. Kibana now updates every 10 seconds. You might have to start the Lambda stream function again to generate more data or you might see data continuing to flow in

You can save your visualizations and build them into dashboards to monitor your infrastructure in near real time.