lake, media: add three benchmark docs with six diagrams

TOC-tidb-cloud-lake.md

@@ -1263,4 +1263,8 @@
   - [Editions](/tidb-cloud-lake/guides/editions.md)
   - [Platforms & Regions](/tidb-cloud-lake/guides/platforms-regions.md)
   - [Pricing & Billing](/tidb-cloud-lake/guides/pricing-billing.md)
+  - Benchmarks
+    - [Data Ingestion Benchmark](/tidb-cloud-lake/guides/benchmark-data-ingestion.md)
+    - [TPC-H SF100 Benchmark](/tidb-cloud-lake/guides/benchmark-tpch-sf100.md)
+    - [TPC-H SF1000 Benchmark](/tidb-cloud-lake/guides/benchmark-tpch-sf1000.md)
   - [Support Services](/tidb-cloud-lake/guides/support-services.md)

tidb-cloud-lake/guides/benchmark-data-ingestion.md

@@ -0,0 +1,182 @@
+---
+title: "{{{ .lake }}} vs. Snowflake: Data Ingestion Benchmark"
+summary: This page presents a benchmark comparison of data ingestion performance and cost between {{{ .lake }}} and Snowflake, focusing on TPC-H SF100 dataset loading, ClickBench Hits dataset loading, and freshness benchmarks.
+---
+
+# {{{ .lake }}} vs. Snowflake: Data Ingestion Benchmark
+
+## Overview
+
+We conducted four specific benchmarks to evaluate {{{ .lake }}} versus Snowflake:
+
+1. **TPC-H SF100 Dataset Loading**: Focuses on loading performance and cost for a large-scale dataset (100GB, ~600 million rows).
+2. **ClickBench Hits Dataset Loading**: Tests efficiency in loading a wide-table dataset (76GB, ~100 million rows, 105 columns), emphasizing challenges associated with high column counts.
+3. **1-Second Freshness**: Measures the platforms' ability to ingest data within a strict 1-second freshness requirement.
+4. **5-Second Freshness**: Compares the platforms' data ingestion capabilities under a 5-second freshness constraint.
+
+## Platforms
+
+- **[Snowflake](https://snowflake.com)**: A well-known cloud data platform emphasizing scalable compute, data sharing.
+- **[{{{ .lake }}}](https://tidbcloud.com)**: A cloud-native data warehouse built on the open-source {{{ .lake }}} project, focusing on scalability and cost-efficiency.
+
+## Benchmark Conditions
+
+Conducted on a `Small-Size` warehouse (16vCPU, AWS us-east-2) using data from the same S3 bucket.
+
+## Performance and Cost Comparison
+
+- **TPC-H SF100 Data**: {{{ .lake }}} offers a **48% cost reduction** over Snowflake.
+- **ClickBench Hits Data**: {{{ .lake }}} achieves a **84% cost reduction**.
+- **1-Second Freshness**: {{{ .lake }}} loads **400 times** more data than Snowflake.
+- **5-Second Freshness**: {{{ .lake }}} loads over **27 times** more data.
+
+## Data Ingestion Benchmarks
+
+![Data loading benchmark](/media/tidb-cloud-lake/data-loading-benchmark.png)
+
+### TPC-H SF100 Dataset
+
+| Metric         | Snowflake | {{{ .lake }}} | Description               |
+| -------------- | --------- | -------------- | ------------------------- |
+| **Total Time** | 695s      | 446s           | Time to load the dataset. |
+| **Total Cost** | $0.77     | $0.40          | Cost of data loading.     |
+
+- Data Volume: 100GB
+- Rows: Approx. 600 million
+
+### ClickBench Hits Dataset
+
+| Metric         | Snowflake | {{{ .lake }}} | Description               |
+| -------------- | --------- | -------------- | ------------------------- |
+| **Total Time** | 51m 17s   | 9m 58s         | Time to load the dataset. |
+| **Total Cost** | $3.42     | $0.53          | Cost of data loading.     |
+
+- Data Volume: 76GB
+- Rows: Approx. 100 million
+- Table Width: 105 columns
+
+## Freshness Benchmarks
+
+![Freshness benchmark](/media/tidb-cloud-lake/freshness-benchmark.png)
+
+### 1-Second Freshness Benchmark
+
+Evaluates the volume of data ingested within a 1-second freshness requirement.
+
+| Metric         | Snowflake | {{{ .lake }}} | Description                                     |
+| -------------- | --------- | -------------- | ----------------------------------------------- |
+| **Total Time** | 1s        | 1s             | Loading time frame.                             |
+| **Total Rows** | 100 Rows  | 40,000 Rows    | Volume of data successfully ingested within 1s. |
+
+### 5-Second Freshness Benchmark
+
+Assesses the volume of data that can be ingested within a 5-second freshness requirement.
+
+| Metric         | Snowflake   | {{{ .lake }}} | Description                                     |
+| -------------- | ----------- | -------------- | ----------------------------------------------- |
+| **Total Time** | 5s          | 5s             | Loading time frame.                             |
+| **Total Rows** | 90,000 Rows | 2,500,000 Rows | Volume of data successfully ingested within 5s. |
+
+## Reproduce the Benchmark
+
+You can reproduce the benchmark by following the steps below.
+
+### Benchmark Environment
+
+The benchmark tests both Snowflake and {{{ .lake }}} under similar conditions:
+
+| Parameter      | Snowflake                                                | {{{ .lake }}}                            |
+| -------------- | -------------------------------------------------------- | ----------------------------------------- |
+| Warehouse Size | Small                                                    | Small                                     |
+| vCPU           | 16                                                       | 16                                        |
+| Price          | [$4/hour](https://www.snowflake.com/en/pricing-options/) | [$3.2/hour](https://www.pingcap.com/pricing/) |
+| AWS Region     | us-east-2                                                | us-east-2                                 |
+| Storage        | AWS S3                                                   | AWS S3                                    |
+
+- The TPC-H SF100 dataset, sourced from [Amazon Redshift](https://github.com/awslabs/amazon-redshift-utils/tree/master/src/CloudDataWarehouseBenchmark/Cloud-DWB-Derived-from-TPCH).
+- The ClickBench dataset, sourced from [ClickBench](https://github.com/ClickHouse/ClickBench).
+
+### Prerequisites
+
+- Have a [Snowflake account](https://signup.snowflake.com)
+- Create a [{{{ .lake }}} account](https://tidbcloud.com/)
+
+### Data Ingestion Benchmark
+
+The data ingestion benchmark can be reproduced using the following steps:
+
+<details>
+  <summary>TPC-H Data Loading</summary>
+
+1. **Snowflake Data Load**:
+
+    - Log into your [Snowflake account](https://app.snowflake.com/).
+    - Create tables corresponding to the TPC-H schema. [SQL Script](https://lakesql-bin.tidbcloud.com/datasets/tpch/tpch-100/snowflake/setup.sql).
+    - Use the `COPY INTO` command to load the data from AWS S3. [SQL Script](https://lakesql-bin.tidbcloud.com/datasets/tpch/tpch-100/snowflake/setup.sql).
+
+2. **{{{ .lake }}} Data Load**:
+
+    - Sign in to your [{{{ .lake }}} account](https://tidbcloud.com).
+    - Create the necessary tables as per the TPC-H schema. [SQL Script](https://lakesql-bin.tidbcloud.com/datasets/tpch/tpch-100/lake/setup.sql).
+    - Use a method similar to Snowflake for loading data from AWS S3. [SQL Script](https://lakesql-bin.tidbcloud.com/datasets/tpch/tpch-100/lake/setup.sql).
+
+</details>
+
+<details>
+  <summary>ClickBench Hits Data Loading</summary>
+
+1. **Snowflake Data Load**:
+
+    - Log into your [Snowflake account](https://app.snowflake.com/).
+    - Create tables corresponding to the `hits` schema. [SQL Script](https://lakesql-bin.tidbcloud.com/datasets/tpch/hits/snowflake/schema.sql).
+    - Use the `COPY INTO` command to load the data from AWS S3. [SQL Script](https://lakesql-bin.tidbcloud.com/datasets/tpch/hits/snowflake/copy.sql).
+
+2. **{{{ .lake }}} Data Load**:
+
+    - Sign in to your [{{{ .lake }}} account](https://tidbcloud.com).
+    - Create the necessary tables as per the `hits` schema. [SQL Script](https://lakesql-bin.tidbcloud.com/datasets/tpch/hits/lake/schema.sql).
+    - Use a method similar to Snowflake for loading data from AWS S3. [SQL Script](https://lakesql-bin.tidbcloud.com/datasets/tpch/hits/lake/copy.sql).
+
+</details>
+
+### Freshness Benchmark
+
+Data generation and ingestion for the freshness benchmark can be reproduced using the following steps:
+
+1. Create an [external stage](/tidb-cloud-lake/sql/create-stage.md#example-2-create-external-stage-with-connection) in {{{ .lake }}}.
+
+    ```sql
+    CREATE STAGE hits_unload_stage
+    URL = 's3://unload/files/'
+    CONNECTION = (
+        ACCESS_KEY_ID = '<your-access-key-id>',
+        SECRET_ACCESS_KEY = '<your-secret-access-key>'
+    );
+    ```
+
+2. Unload data to the external stage.
+
+    ```sql
+    CREATE or REPLACE FILE FORMAT tsv_unload_format_gzip
+        TYPE = TSV,
+        COMPRESSION = gzip;
+
+    COPY INTO @hits_unload_stage
+    FROM (
+        SELECT *
+        FROM hits limit <the-rows-you-want>
+    )
+    FILE_FORMAT = (FORMAT_NAME = 'tsv_unload_format_gzip')
+    DETAILED_OUTPUT = true;
+    ```
+
+3. Load data from the external stage to the `hits` table.
+
+    ```sql
+    COPY INTO hits
+        FROM @hits_unload_stage
+        PATTERN = '.*[.]tsv.gz'
+        FILE_FORMAT = (TYPE = TSV,  COMPRESSION=auto);
+    ```
+
+4. Measure results from the dashboard.