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new 23c9de5f85 [README](fix) update pictures links in README.md (#11891)
23c9de5f85 is described below
commit 23c9de5f857f04833799b9b039642a52f2e0b13c
Author: Luzhijing <[email protected]>
AuthorDate: Fri Aug 19 21:32:48 2022 +0800
[README](fix) update pictures links in README.md (#11891)
---
README.md | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/README.md b/README.md
index 42f21a8499..0900b07ba9 100644
--- a/README.md
+++ b/README.md
@@ -46,7 +46,7 @@ Based on this, Apache Doris can better meet the scenarios of
report analysis, ad
As shown in the figure below, after various data integration and processing,
the data sources are usually stored in the real-time data warehouse Apache
Doris and the offline data lake or data warehouse (in Apache Hive, Apache
Iceberg or Apache Hudi).
-<img
src="https://doris.apache.org/assets/images/what-is-doris-2ed5ac7fffa3799871d5d33993b1de09.png";>
+<img
src="https://dev-to-uploads.s3.amazonaws.com/uploads/articles/sekvbs5ih5rb16wz6n9k.png";>
Apache Doris is widely used in the following scenarios:
@@ -74,7 +74,7 @@ The overall architecture of Apache Doris is shown in the
following figure. The D
Both types of processes are horizontally scalable, and a single cluster can
support up to hundreds of machines and tens of petabytes of storage capacity.
And these two types of processes guarantee high availability of services and
high reliability of data through consistency protocols. This highly integrated
architecture design greatly reduces the operation and maintenance cost of a
distributed system.
-
+
Apache Doris adopts MySQL protocol, highly compatible with MySQL dialect, and
supports standard SQL. Users can access Doris through various client tools and
support seamless connection with BI tools.
@@ -105,11 +105,11 @@ Apache Doris also supports strong consistent materialized
views, where updates a
In terms of query engine, Apache Doris adopts the MPP model, with parallel
execution between and within nodes, and also supports distributed shuffle join
for multiple large tables, which can better cope with complex queries.
-
+
The Doris query engine is vectorized, and all memory structures can be laid
out in a columnar format to achieve significant reductions in virtual function
calls, improved Cache hit rates, and efficient use of SIMD instructions.
Performance in wide table aggregation scenarios is 5–10 times higher than in
non-vectorized engines.
-
+
Apache Doris uses Adaptive Query Execution technology, which can dynamically
adjust the execution plan based on runtime statistics, such as runtime filter
technology to generate filters to push to the probe side at runtime and to
automatically penetrate the filters to the probe side which drastically reduces
the amount of data in the probe and speeds up join performance. Doris' runtime
filter supports In/Min/Max/Bloom filter.
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