From a01a30dece65f0391210c1a60e6ef17d4270581a Mon Sep 17 00:00:00 2001 From: Yingjun Wu Date: Sat, 6 Jan 2024 21:47:24 -0800 Subject: [PATCH] chore: Update README.md (#14371) Co-authored-by: hengm3467 <100685635+hengm3467@users.noreply.github.com> --- README.md | 22 ++++++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 13d90a85ed7c7..f9f4265593695 100644 --- a/README.md +++ b/README.md @@ -60,7 +60,8 @@ -RisingWave is a distributed SQL streaming database that enables simple, efficient, and reliable processing of streaming data. +RisingWave is a distributed SQL streaming database engineered to provide the simplest and most cost-efficient approach for processing and managing streaming data with utmost reliability. + ![RisingWave](https://github.com/risingwavelabs/risingwave-docs/blob/main/docs/images/new_archi_grey.png) @@ -98,7 +99,7 @@ For **Kubernetes deployments**, please refer to [Kubernetes with Helm](https://d ## Why RisingWave for stream processing? -RisingWave specializes in providing **incrementally updated, consistent materialized views** — a persistent data structure that represents the results of stream processing. RisingWave significantly reduces the complexity of building stream processing applications by allowing developers to express intricate stream processing logic through cascaded materialized views. Furthermore, it allows users to persist data directly within the system, eliminating the need to deliver results to external databases for storage and query serving. +RisingWave provides users with a comprehensive set of frequently used stream processing features, including exactly-once consistency, [time window functions](https://docs.risingwave.com/docs/current/sql-function-time-window/), [watermarks](https://docs.risingwave.com/docs/current/watermarks/), and more. It specializes in providing **incrementally updated, consistent materialized views** — a persistent data structure that represents the results of stream processing. RisingWave significantly reduces the complexity of building stream processing applications by allowing developers to express intricate stream processing logic through cascaded materialized views. Furthermore, it allows users to persist data directly within the system, eliminating the need to deliver results to external databases for storage and query serving. ![Real-time Data Pipelines without or with RisingWave](https://github.com/risingwavelabs/risingwave/assets/100685635/414afbb7-5187-410f-9ba4-9a640c8c6306) @@ -122,12 +123,25 @@ Compared to existing stream processing systems like [Apache Flink](https://flink * **Instant failure recovery** * RisingWave's state management mechanism also allows it to recover from failure in seconds, not minutes or hours. +### RisingWave as a database +RisingWave is fundamentally a database that **extends beyond basic streaming data processing capabilities**. It excels in **the effective management of streaming data**, making it a trusted choice for data persistence and powering online applications. RisingWave offers an extensive range of database capabilities, which include: + +* High availability +* Serving highly concurrent queries +* Role-based access control (RBAC) +* Integration with data modeling tools, such as [dbt](https://docs.risingwave.com/docs/current/use-dbt/) +* Integration with database management tools, such as [Dbeaver](https://docs.risingwave.com/docs/current/dbeaver-integration/) +* Integration with BI tools, such as [Grafana](https://docs.risingwave.com/docs/current/grafana-integration/) +* Schema change +* Processing of semi-structured data + + ## RisingWave's limitations RisingWave isn’t a panacea for all data engineering hurdles. It has its own set of limitations: * **No programmable interfaces** - * RisingWave does not provide low-level APIs in languages like Java and Scala, and does not allow users to manage internal states manually (unless you want to hack!). For coding in Java, Scala, and other languages, please consider using RisingWave's User-Defined Functions (UDF). + * RisingWave does not provide low-level APIs in languages like Java and Scala, and does not allow users to manage internal states manually (unless you want to hack!). _For coding in Java, Python, and other languages, please consider using RisingWave's [User-Defined Functions (UDF)](https://docs.risingwave.com/docs/current/user-defined-functions/)_. * **No support for transaction processing** - * RisingWave isn’t cut out for transactional workloads, thus it’s not a viable substitute for operational databases dedicated to transaction processing. However, it supports read-only transactions, ensuring data freshness and consistency. It also comprehends the transactional semantics of upstream database Change Data Capture (CDC). + * RisingWave isn’t cut out for transactional workloads, thus it’s not a viable substitute for operational databases dedicated to transaction processing. _However, it supports [read-only transactions](https://docs.risingwave.com/docs/current/transactions/#read-only-transactions), ensuring data freshness and consistency. It also comprehends the transactional semantics of upstream database [Change Data Capture (CDC)](https://docs.risingwave.com/docs/current/transactions/#transactions-within-a-cdc-table)_. * **Not tailored for ad-hoc analytical queries** * RisingWave's row store design is tailored for optimal stream processing performance rather than interactive analytical workloads. Hence, it's not a suitable replacement for OLAP databases. Yet, a reliable integration with many OLAP databases exists, and a collaborative use of RisingWave and OLAP databases is a common practice among many users.