Electric vehicle station-data streaming ingestion tutorial, using Kinesis - Amazon Redshift
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Electric vehicle station-data streaming ingestion tutorial, using Kinesis

This procedure demonstrates how to ingest data from a Kinesis stream named ev_station_data, which contains consumption data from different EV charging stations, in JSON format. The schema is well defined. The example shows how to store the data as raw JSON and also how to convert the JSON data to Amazon Redshift data types as it's ingested.

Producer setup

  1. Using Amazon Kinesis Data Streams, follow the steps to create a stream named ev_station_data. Choose On-demand for the Capacity mode. For more information, see Creating a Stream via the Amazon Management Console.

  2. The Amazon Kinesis Data Generator can help you generate test data for use with your stream. Follow the steps detailed in the tool to get started, and use the following data template for generating your data:

    { "_id" : "{{random.uuid}}", "clusterID": "{{random.number( { "min":1, "max":50 } )}}", "connectionTime": "{{date.now("YYYY-MM-DD HH:mm:ss")}}", "kWhDelivered": "{{commerce.price}}", "stationID": "{{random.number( { "min":1, "max":467 } )}}", "spaceID": "{{random.word}}-{{random.number( { "min":1, "max":20 } )}}", "timezone": "America/Los_Angeles", "userID": "{{random.number( { "min":1000, "max":500000 } )}}" }

    Each JSON object in the stream data has the following properties:

    { "_id": "12084f2f-fc41-41fb-a218-8cc1ac6146eb", "clusterID": "49", "connectionTime": "2022-01-31 13:17:15", "kWhDelivered": "74.00", "stationID": "421", "spaceID": "technologies-2", "timezone": "America/Los_Angeles", "userID": "482329" }

Amazon Redshift setup

These steps show you how to configure the materialized view to ingest data.

  1. Create an external schema to map the data from Kinesis to a Redshift object.

    CREATE EXTERNAL SCHEMA evdata FROM KINESIS IAM_ROLE 'arn:aws:iam::0123456789:role/redshift-streaming-role';

    For information about how to configure the IAM role, see Getting started with streaming ingestion from Amazon Kinesis Data Streams.

  2. Create a materialized view to consume the stream data. The following examples show both methods of defining materialized views to ingest the JSON source data.

    First, store stream records in semi-structured SUPER format. In this example, the JSON source is stored in Redshift without converting to Redshift types.

    CREATE MATERIALIZED VIEW ev_station_data AS SELECT approximate_arrival_timestamp, partition_key, shard_id, sequence_number, json_parse(kinesis_data) as payload FROM evdata."ev_station_data" WHERE can_json_parse(kinesis_data);

    In contrast, in the following materialized view definition, the materialized view has a defined schema in Redshift. The materialized view is distributed on the UUID value from the stream and is sorted by the approximatearrivaltimestamp value.

    CREATE MATERIALIZED VIEW ev_station_data_extract DISTKEY(6) sortkey(1) AUTO REFRESH YES AS SELECT refresh_time, approximate_arrival_timestamp, partition_key, shard_id, sequence_number, json_extract_path_text(from_varbyte(kinesis_data,'utf-8'),'_id',true)::character(36) as ID, json_extract_path_text(from_varbyte(kinesis_data,'utf-8'),'clusterID',true)::varchar(30) as clusterID, json_extract_path_text(from_varbyte(kinesis_data,'utf-8'),'connectionTime',true)::varchar(20) as connectionTime, json_extract_path_text(from_varbyte(kinesis_data,'utf-8'),'kWhDelivered',true)::DECIMAL(10,2) as kWhDelivered, json_extract_path_text(from_varbyte(kinesis_data,'utf-8'),'stationID',true)::DECIMAL(10,2) as stationID, json_extract_path_text(from_varbyte(kinesis_data,'utf-8'),'spaceID',true)::varchar(100) as spaceID, json_extract_path_text(from_varbyte(kinesis_data, 'utf-8'),'timezone',true)::varchar(30)as timezone, json_extract_path_text(from_varbyte(kinesis_data,'utf-8'),'userID',true)::varchar(30) as userID FROM evdata."ev_station_data" WHERE LENGTH(kinesis_data) < 65355;

Query the stream

  1. Query the refreshed materialized view to get usage statistics.

    SELECT to_timestamp(connectionTime, 'YYYY-MM-DD HH24:MI:SS') as connectiontime ,SUM(kWhDelivered) AS Energy_Consumed ,count(distinct userID) AS #Users from ev_station_data_extract group by to_timestamp(connectionTime, 'YYYY-MM-DD HH24:MI:SS') order by 1 desc;
  2. View results.

    connectiontime energy_consumed #users 2022-02-08 16:07:21+00 4139 10 2022-02-08 16:07:20+00 5571 10 2022-02-08 16:07:19+00 8697 20 2022-02-08 16:07:18+00 4408 10 2022-02-08 16:07:17+00 4257 10 2022-02-08 16:07:16+00 6861 10 2022-02-08 16:07:15+00 5643 10 2022-02-08 16:07:14+00 3677 10 2022-02-08 16:07:13+00 4673 10 2022-02-08 16:07:11+00 9689 20