Azure Stream Analytics to Event Hub Very Slow - performance

Question

Stream Analytics job takes several minutes to output results to an Event Hub.
A Stateless Web API Azure Service Fabric application is distributed across 8 nodes. The application is very simple, consisting of a single Controller, which:
Receives a series of JSON objects
Initialises a series of EventData instances that wrap each JSON object
Sets the PartitionKey property of each EventData isntance to the machine-name value
Publishes the JSON objects as a single batch of EventData instances to Azure Event Hub
The JSON payload is a simple series of IP addresses and time-stamps, as follows:
[{
"IPAddress": "10.0.0.2",
"Time": "2016-08-17T12:00:01",
"MachineName": "MACHINE01"
}, {
"IPAddress": "10.0.0.3",
"Time": "2016-08-17T12:00:02",
"MachineName": "MACHINE01"
}]
Once received, the Event Hub acts as an input to a Stream Analytics job, which executes the following Query:
SELECT
IPAddress, COUNT(*) AS Total, MachineName
INTO
Output
FROM
Input TIMESTAMP BY TIME
PARTITION BY PartitionId
GROUP BY
TUMBLINGWINDOW(MINUTE, 1), IPAddress, MachineName, PartitionId
HAVING Total >= 2
Note that the Query is partitioned by PartitionId, where PartitionId is set to the machine-name of the originating Service Fabric application. There will therefore be a maximum of 8 PartitionKeys
There are 8 individual Service Fabric instances, and 8 corresponding Partitions assigned to the input Event Hub.
Finally, The Stream Analytics job outputs the result to a 2nd Event Hub. Again, this Event Hub has 8 Partitions. The Stream Analytics Query retains the machine-name, which is used as the PartitionKey of the output Event Hub. The output format is JSON.
At best, the process takes 30-60 seconds, and sometimes several minutes, for a single HTTP request to reach the output Event Hub. The bottleneck seems to be the Stream Analytics job, given that the ASP.NET application publishes the EventData batches in sub-second timescales.
Edit:
Applying a custom field to Timestamp By adds a great degree of latency, when coupled with a Group By clause. I have achieved acceptable latency (1-2 seconds) when removing the Timestamp By clause.
The optimal Query is as follows:
SELECT
Count(*) AS Total, IPAddress
FROM
Input
Partition by PartitionId
GROUP BY TUMBLINGWINDOW(MINUTE, 1), IPAddress, PartitionId
However, adding a Having clause, results in latency increasing to 10-20 seconds:
SELECT
Count(*) AS Total, IPAddress
FROM
Input
Partition by PartitionId
GROUP BY TUMBLINGWINDOW(MINUTE, 1), IPAddress, PartitionId
HAVING Total >= 10
Without the ability to aggregate data within the Query, using a Having clause, in a timely fashion, seems to defeat the purpose.
Incidentally, Streaming Units, Partitions, Input and Output are configured optimally, as per this guide to achieving parallelism with Stream Analytics.