Update README and sample docker compose

README.md

@@ -76,26 +76,27 @@ The [Docker Compose stack](https://github.com/timeplus-io/proton/tree/develop/ex
 
 ### Timeplus Cloud:
 
-Don't want to setup by yourself? Try Timeplus Proton in [Cloud](https://us-west-2.timeplus.cloud/)
+Don't want to setup by yourself? Try Timeplus in [Cloud](https://us-west-2.timeplus.cloud/)
 
 
 ### 🔎 Usage
 SQL is the main interface. You can start a new terminal window with `proton client` to start the SQL shell.
 > [!NOTE]
 > You can also integrate Timeplus Proton with Python/Java/Go SDK, REST API, or BI plugins. Please check <a href="#-integrations"><strong>Integrations</strong></a>
 
-In the `proton client`, you can write SQL to create [External Stream for Kafka](https://docs.timeplus.com/proton-kafka) or [External Table for ClickHouse](https://docs.timeplus.com/proton-clickhouse-external-table). You can also run the following SQL to create a stream of random data:
+In the `proton client`, you can write SQL to create [External Stream for Kafka](https://docs.timeplus.com/proton-kafka) or [External Table for ClickHouse](https://docs.timeplus.com/proton-clickhouse-external-table).
+
+You can also run the following SQL to create a stream of random data:
 
 ```sql
 -- Create a stream with random data
 CREATE RANDOM STREAM devices(
   device string default 'device'||to_string(rand()%4),
-  temperature float default rand()%1000/10)
-```
-```sql
+  temperature float default rand()%1000/10);
+
 -- Run the streaming SQL
 SELECT device, count(*), min(temperature), max(temperature)
-FROM devices GROUP BY device
+FROM devices GROUP BY device;
 ```
 
 You should see data like the following:
@@ -119,8 +120,8 @@ What features are available with Timeplus Proton versus Timeplus Enterprise?
 |                               | **Timeplus Proton**                                                                                                                                                                    | **Timeplus Enterprise**                                                                                                                                                                                                          |
 | ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | **Deployment**                | <ul><li>Single-node Docker image</li><li>Single binary on Mac/Linux</li></ul>                                                                                                          | <ul><li>Single node, or</li><li>Cluster</li><li>Kubernetes-based self-hosting, or</li><li>Fully-managed cloud service</li></ul>                                                                               |
-| **Data sources**              | <ul><li>Random streams</li><li>External streams to Apache Kafka, Confluent Cloud, Redpanda</li><li>Streaming ingestion via REST API (compact mode only)</li></ul> | <ul><li>Everything in Timeplus Proton</li><li>WebSocket and HTTP Stream</li><li>NATS</li><li>Apache Pulsar</li><li>CSV upload</li><li>Streaming ingestion via REST API (with API key and flexible modes)</li><li>Hundreds of connectors from Redpanda Connect</li></ul> |
-| **Data destinations (sinks)** | <ul><li>External streams to Apache Kafka, Confluent Cloud, Redpanda</li></ul>                                                                                                          | <ul><li>Everything in Timeplus Proton</li><li>Apache Pulsar</li><li>Slack</li><li>Webhook</li><li>Exchange data with other Timeplus Enterprise or Proton deployments</li></ul>                                                                                                      |
+| **Data sources**              | <ul><li>Random streams</li><li>External streams to Apache Kafka, Confluent Cloud, Redpanda</li><li>Streaming ingestion via REST API (compact mode only)</li></ul> | <ul><li>Everything in Timeplus Proton</li><li>External streams to another Timeplus Proton or Timeplus Enterprise deployment</li><li>WebSocket and HTTP Stream</li><li>NATS</li><li>CSV upload</li><li>Streaming ingestion via REST API (with API key and flexible modes)</li><li>Hundreds of connectors from Redpanda Connect</li></ul> |
+| **Data destinations (sinks)** | <ul><li>External streams to Apache Kafka, Confluent Cloud, Redpanda</li></ul>                                                                                                          | <ul><li>Everything in Timeplus Proton</li><li>External streams to another Timeplus Proton or Timeplus Enterprise deployment</li><li>Slack</li><li>Webhook</li><li>Hundreds of connectors from Redpanda Connect</li></ul>                                                                                                      |
 | **Support**                   | <ul><li>Community support from GitHub and Slack</li></ul>                                                                                                                              | <ul><li>Enterprise support via email, Slack, and Zoom, with a SLA</li></ul>                                                                                                                                                      |
 
 ## 🧩 Integrations
@@ -133,6 +134,7 @@ The following drivers are available:
 Integrations with other systems:
 
 * ClickHouse https://docs.timeplus.com/proton-clickhouse-external-table
+* Docker and Testcontainers https://docs.timeplus.com/tutorial-testcontainers-java
 * Sling https://docs.timeplus.com/sling
 * Grafana https://github.com/timeplus-io/proton-grafana-source
 * Metabase  https://github.com/timeplus-io/metabase-proton-driver

docker/compose/docker-compose.yml

@@ -2,7 +2,7 @@ version: "3"
 
 services:
   proton-server:
-    image: ghcr.io/timeplus-io/proton:latest
+    image: d.timeplus.com/timeplus-io/proton:latest
     pull_policy: always
     ports:
       - "8123:8123" # HTTP
@@ -42,14 +42,14 @@ services:
       - redpanda
       - start
       - --smp
-      - '1'
+      - "1"
       - --memory
       - 1G
       - --reserve-memory
       - 0M
       - --overprovisioned
       - --node-id
-      - '0'
+      - "0"
       - --kafka-addr
       - PLAINTEXT://0.0.0.0:29092,OUTSIDE://0.0.0.0:9092
       - --advertise-kafka-addr

examples/carsharing/README.md

@@ -2,7 +2,7 @@
 
 
 
-This docker compose file demonstrates some typical query patterns that you can achieve in Proton to solve various use cases. 
+This docker compose file demonstrates some typical query patterns that you can achieve in Proton to solve various use cases.
 
 For more details, please check https://docs.timeplus.com/usecases
 
@@ -12,7 +12,7 @@ For more details, please check https://docs.timeplus.com/usecases
 
 Simply run `docker compose up` in this folder. Two docker containers in the stack:
 
-1. ghcr.io/timeplus-io/proton:latest, as the streaming database
+1. d.timeplus.com/timeplus-io/proton:latest, as the streaming database
 2. timeplus/cardemo:latest, as the data generator
 
 ## Customer Scenario and Data Model
@@ -76,19 +76,19 @@ Please check https://docs.timeplus.com/usecases for more sample queries.
 
 ```sql
 -- List live data
-SELECT * FROM car_live_data; 
+SELECT * FROM car_live_data;
 
 -- Filter data
-SELECT time,cid,gas_percent FROM car_live_data WHERE gas_percent < 25;  
+SELECT time,cid,gas_percent FROM car_live_data WHERE gas_percent < 25;
 
 -- Downsampling
 SELECT window_start,cid, avg(gas_percent) AS avg_gas_percent,avg(speed_kmh) AS avg_speed FROM
-tumble(car_live_data,1m) GROUP BY window_start, cid; 
+tumble(car_live_data,1m) GROUP BY window_start, cid;
 
 -- Create materlized view
 CREATE MATERIALIZED VIEW car_live_data_1min as
-SELECT window_start AS time,cid, avg(gas_percent) AS avg_gas,avg(speed_kmh) AS avg_speed 
-FROM tumble(car_live_data,1m) GROUP BY window_start, cid; 
+SELECT window_start AS time,cid, avg(gas_percent) AS avg_gas,avg(speed_kmh) AS avg_speed
+FROM tumble(car_live_data,1m) GROUP BY window_start, cid;
 SELECT * FROM car_live_data_1min;
 
 -- Top K
@@ -99,9 +99,8 @@ SELECT avg(gap) FROM
 ( SELECT
     date_diff('second', bookings.booking_time, trips.start_time) AS gap
   FROM bookings
-  INNER JOIN trips ON (bookings.bid = trips.bid) 
+  INNER JOIN trips ON (bookings.bid = trips.bid)
      AND date_diff_within(2m, bookings.booking_time, trips.start_time)
 ) WHERE _tp_time >= now()-1d;
 
 ```
-

examples/cdc/README.md

@@ -6,7 +6,7 @@ This docker compose file demonstrates how to capture live database change from a
 
 Simply run `docker compose up` in this folder. Five docker containers in the stack:
 
-1. ghcr.io/timeplus-io/proton:latest, as the streaming database.
+1. d.timeplus.com/timeplus-io/proton:latest, as the streaming database.
 2. docker.redpanda.com/redpandadata/redpanda, as the Kafka compatiable streaming message bus
 3. docker.redpanda.com/redpandadata/console, as the web UI to explore data in Kafka/Redpanda
 4. debezium/connect, as the CDC engine to read changes from OLTP and send data to Kafka/Redpanda

examples/clickhouse/README.md

@@ -8,7 +8,7 @@ A YouTube video tutorial is available for visual learners: https://youtu.be/ga_D
 
 Simply run `docker compose up` in this folder. Three docker containers in the stack:
 
-1. ghcr.io/timeplus-io/proton:latest, as the streaming SQL engine. 
+1. d.timeplus.com/timeplus-io/proton:latest, as the streaming SQL engine.
 2. clickhouse/clickhouse-server:latest
 3. quay.io/cloudhut/owl-shop:latest, as the data generator. [Owl Shop](https://github.com/cloudhut/owl-shop) is an imaginary ecommerce shop that simulates microservices exchanging data via Apache Kafka.
 4. docker.redpanda.com/redpandadata/redpanda, as the Kafka compatiable streaming message bus

examples/coinbase/README.md

@@ -2,15 +2,15 @@
 
 
 
-This docker compose file demonstrates how to ingest WebSocket data into Proton by using Benthos pipeline. 
+This docker compose file demonstrates how to ingest WebSocket data into Proton by using Benthos pipeline.
 
 
 
 ## Start the stack
 
 Simply run `docker compose up` in this folder. Three docker containers in the stack:
 
-1. ghcr.io/timeplus-io/proton:latest, as the streaming database
+1. d.timeplus.com/timeplus-io/proton:latest, as the streaming database
 2. jeffail/benthos:latest, a [Benthos](https://www.benthos.dev/) service as the data pipeline
 3. init container, create the tickers stream when Proton database server is ready
 
@@ -54,7 +54,7 @@ output:
   http_client:
     url: http://proton:8123/proton/v1/ingest/streams/tickers
     verb: POST
-    headers: 
+    headers:
       Content-Type: application/json
     batching:
       count: 10
@@ -85,5 +85,3 @@ WHERE
 GROUP BY
   window_start, product_id
 ```
-
-

examples/ecommerce/README.md

@@ -7,7 +7,7 @@ For more details, please check https://docs.timeplus.com/proton-kafka#tutorial
 ## Start the example
 
 Simply run `docker compose up` in this folder. Four docker containers in the stack:
-1. ghcr.io/timeplus-io/proton:latest, as the streaming database.
+1. d.timeplus.com/timeplus-io/proton:latest, as the streaming database.
 2. quay.io/cloudhut/owl-shop:latest, as the data generator. [Owl Shop](https://github.com/cloudhut/owl-shop) is an imaginary ecommerce shop that simulates microservices exchanging data via Apache Kafka.
 3. docker.redpanda.com/redpandadata/redpanda, as the Kafka compatiable streaming message bus
 4. docker.redpanda.com/redpandadata/console, as the web UI to explore data in Kafka/Redpanda
@@ -20,7 +20,7 @@ Run the following commands:
 ```sql
 -- Create externarl stream to read data from Kafka/Redpanda
 CREATE EXTERNAL STREAM frontend_events(raw string)
-SETTINGS type='kafka', 
+SETTINGS type='kafka',
          brokers='redpanda:9092',
          topic='owlshop-frontend-events';
 

examples/fraud_detection/README.md

@@ -5,12 +5,11 @@ This docker compose file demonstrates how to leverage proton to build a real-tim
 ## Start the example
 
 Simply run `docker compose up` in this folder. three docker containers in the stack:
-1. ghcr.io/timeplus-io/proton:latest, as the streaming database.
+1. d.timeplus.com/timeplus-io/proton:latest, as the streaming database.
 2. timeplus/fraud:latest, a online payment transaction data generator
 3. jupyter/scipy-notebook:latest, jupyter notebook
 
 
 ## Run Notebook
 
 Visit `http://localhost:8888/notebooks/work/fraud_detection.ipynb` to access the notebook. And then just follow the code in the notebook step by step.
-

examples/grafana/README.md

@@ -8,7 +8,7 @@ A YouTube video tutorial is available for visual learners: https://www.youtube.c
 
 Simply run `docker compose up` in this folder. Three docker containers in the stack:
 
-1. ghcr.io/timeplus-io/proton:latest, as the streaming SQL engine. Port 8463 and 3218 are exposed so that Grafana can connect to it.
+1. d.timeplus.com/timeplus-io/proton:latest, as the streaming SQL engine. Port 8463 and 3218 are exposed so that Grafana can connect to it.
 2. timeplus/cardemo:latest, as the data generator
 3. grafana/grafana:latest, with pre-configured Proton dashboard and a live dashboard
 
@@ -20,19 +20,19 @@ Please check https://docs.timeplus.com/usecases for more sample queries.
 
 ```sql
 -- List live data
-SELECT * FROM car_live_data; 
+SELECT * FROM car_live_data;
 
 -- Filter data
-SELECT time,cid,gas_percent FROM car_live_data WHERE gas_percent < 25;  
+SELECT time,cid,gas_percent FROM car_live_data WHERE gas_percent < 25;
 
 -- Downsampling
 SELECT window_start,cid, avg(gas_percent) AS avg_gas_percent,avg(speed_kmh) AS avg_speed FROM
-tumble(car_live_data,1m) GROUP BY window_start, cid; 
+tumble(car_live_data,1m) GROUP BY window_start, cid;
 
 -- Create materlized view
 CREATE MATERIALIZED VIEW car_live_data_1min as
-SELECT window_start AS time,cid, avg(gas_percent) AS avg_gas,avg(speed_kmh) AS avg_speed 
-FROM tumble(car_live_data,1m) GROUP BY window_start, cid; 
+SELECT window_start AS time,cid, avg(gas_percent) AS avg_gas,avg(speed_kmh) AS avg_speed
+FROM tumble(car_live_data,1m) GROUP BY window_start, cid;
 SELECT * FROM car_live_data_1min;
 
 -- Top K
@@ -43,9 +43,8 @@ SELECT avg(gap) FROM
 ( SELECT
     date_diff('second', bookings.booking_time, trips.start_time) AS gap
   FROM bookings
-  INNER JOIN trips ON (bookings.bid = trips.bid) 
+  INNER JOIN trips ON (bookings.bid = trips.bid)
      AND date_diff_within(2m, bookings.booking_time, trips.start_time)
 ) WHERE _tp_time >= now()-1d;
 
 ```
-

examples/hackernews/README.md

@@ -5,7 +5,7 @@ Inspired by https://bytewax.io/blog/polling-hacker-news, you can call Hacker New
 ## Start the example
 
 Simply run `docker compose up` in this folder and it will start
-1. ghcr.io/timeplus-io/proton:latest, with pre-configured streams, materialized views and views.
+1. d.timeplus.com/timeplus-io/proton:latest, with pre-configured streams, materialized views and views.
 2. timeplus/hackernews_bytewax:latest, leveraging [bytewax](https://bytewax.io) to call Hacker News HTTP API with Bytewax and send latest news to Proton. [Source code](https://github.com/timeplus-io/proton-python-driver/tree/develop/example/bytewax)
 3. A pre-configured Grafana instance to visulaize the live data.
 
@@ -36,7 +36,7 @@ With all those streams and views, you can query the data in whatever ways, e.g.
 ```sql
 select * from comment;
 
-select 
+select
     story._tp_time as story_time,comment._tp_time as comment_time,
     story.id as story_id, comment.id as comment_id,
     substring(story.title,1,20) as title,substring(comment.raw:text,1,20) as comment

examples/jdbc/README.md

@@ -6,7 +6,7 @@ This docker compose file demonstrates how to connect to Proton via JDBC driver.
 
 Simply run `docker compose up` in this folder. Two docker containers in the stack:
 
-1. ghcr.io/timeplus-io/proton:latest, as the streaming database. Port 8123 is exposed so that JDBC driver can connect to it.
+1. d.timeplus.com/timeplus-io/proton:latest, as the streaming database. Port 8123 is exposed so that JDBC driver can connect to it.
 2. timeplus/cardemo:latest, as the data generator
 
 Please note port 8123 from the Proton container is exposed to the host. You need this port to connect to Proton via JDBC driver.
@@ -55,7 +55,7 @@ public class App {
             }
             return count;
         }
-    }    
+    }
     public static void main(String[] args) {
         String url = "jdbc:proton://localhost:8123";
         String user = System.getProperty("user", "default");
@@ -85,7 +85,7 @@ First add the Proton JDBC driver to DBeaver. Taking DBeaver 23.2.3 as an example
 * Default User: default
 * Allow Empty Password
 
-In the "Libaries" tab, click "Add Artifact" and type `com.timeplus:proton-jdbc:0.6.0`. Click the "Find Class" button to load the class. 
+In the "Libaries" tab, click "Add Artifact" and type `com.timeplus:proton-jdbc:0.6.0`. Click the "Find Class" button to load the class.
 
 Create a new database connection, choose "Timeplus Proton" and accept the default settings. Click the "Test Connection.." to verify the connection is okay.