Infrastructure Landscape Application Middle Platform Data Middle Platform(Modern Data Stack) Middleware Ingestion/Transport Storage Query Processing Transformation Analysis/Output Lakehouse Dashboards Embedded Operational Databases A16Z Analytics Serving OLAP Real time OLAP Clickhouse Druid Reverse ETL Stream Processing OLTP NoSQL/Multi Model Lindorm HTAP PolarDB PolarDB TSDB Search Elasticsearch

data silos or integration bottlenecks. A modern Real-Time UDL typically includes: Real-time data ingestion from structured, semi-structured and unstructured sources (IoT, logs, event streams). Multi-model real- time insights, delaying critical business actions. Optimized Ingestion, Indexing and Querying Real-Time UDLs index data upon ingestion and execute complex queries on very large data sets in the sub-second for many applications. Continuous data flow: CrateDB handles high-throughput, high-velocity data ingestion and analysis, ensuring businesses can respond dynamically to changing conditions. Instant data insights:

Multiple SCD models (slides after that...) • Good “dove-tailing” with subsequent “sematic” ETL steps Ingestion into relational data lake semantic layer (cubes, marts etc..) ODS, e.g. redshift, greenplum uncompacted data in target S3 • HVR also delivers meta-data as hive external tables definitions Ingestion into data lake storage compaction storage, e.g. CSV or Avro files on S3 semantic layer (cubes need SQL access will get ‘basic’ replication Added value from HVR • Easy to setup • Performant Ingestion into data lake using streaming compaction semantic layer (cubes, marts etc..) ETL ODS, e.g

rewrite. Optimization of ShardingSphere ShardingSphere has optimized in two ways. Firstly, it adopts stream process + merger ordering to avoid excessive memory occupation. SQL rewrite unavoidably occupies DATA_CONSISTENCY_CHECKER (algorithmDefinition) rateLimiter: RATE_LIMITER (algorithmDefinition) streamChannel: STREAM_CHANNEL (algorithmDefinition) workerThread: WORKER_THREAD=intValue batchSize: BATCH_SIZE=intValue WORKER_THREAD=40, BATCH_SIZE=1000 ), 5.2. ShardingSphere-Proxy 170 Apache ShardingSphere document, v5.1.1 STREAM_CHANNEL(TYPE(NAME=MEMORY, PROPERTIES("block-queue-size"=10000))), COMPLETION_DETECTOR(TYPE(NAME=IDLE

rewrite. Optimization of ShardingSphere ShardingSphere has optimized in two ways. Firstly, it adopts stream process + merger ordering to avoid excessive memory occupation. SQL rewrite unavoidably occupies DATA_CONSISTENCY_CHECKER (algorithmDefinition) rateLimiter: RATE_LIMITER (algorithmDefinition) streamChannel: STREAM_CHANNEL (algorithmDefinition) workerThread: WORKER_THREAD=intValue batchSize: BATCH_SIZE=intValue INPUT( WORKER_THREAD=40, BATCH_SIZE=1000 ), OUTPUT( WORKER_THREAD=40, BATCH_SIZE=1000 ), STREAM_CHANNEL(TYPE(NAME=MEMORY, PROPERTIES("block-queue-size"=10000))), COMPLETION_DETECTOR(TYPE(NAME=IDLE

cleaning up stale regions might accidentally delete valid data #17258 @hbisheng • Fix the issue that Ingestion picked level and Compaction Job Size(files) are displayed incorrectly in the TiKV dashboard in TiDB Bin- log 6 Deprecated Y Y Y Y Y Y Y Y Y Y Change data cap- ture (CDC) Y Y Y Y Y Y Y Y Y Y Y Stream data to Ama- zon S3, GCS, Azure Blob Stor- age, and NFS through TiCDC Y Y Y Y Y E N N N N N transactions different processing engines based on the specific business. • Real-time stream processing When using TiDB in real-time stream processing scenarios, TiDB ensures that all the data flowed in constantly

TiCDC and PITR. 94 Data im- port and ex- port 8.4 8.3 8.2 8.1 7.5 7.1 6.5 6.1 5.4 5.3 5.2 5.1 Stream data to Ama- zon S3, GCS, Azure Blob Stor- age, and NFS through TiCDC Y Y Y Y Y Y E N N N N N TiCDC different processing engines based on the specific business. • Real-time stream processing When using TiDB in real-time stream processing scenarios, TiDB ensures that all the data flowed in constantly in the URL. Then, resultset is automatically closed but the result set to be read in the previous stream- ing query is lost. • The second method: Use Cursor Fetch by first setting FetchSize as a positive

major virtualized environments like VMware, KVM and XEN. • Red Hat Enterprise Linux 8.0, CentOS 8 Stream, and Oracle Enterprise Linux 8.0 are not supported yet as the testing of these platforms is in progress de- fault ini- tial- iza- tion com- mitTS -1 as the lat- est times- tamp; Con- fig- ure the down- stream tar- get TiDB as 10.0.1.12:4000 �→ 123 4.3.4.1.1 Topology templates • The simple template for Aggregating the count of rows both in TiDB (StreamAgg_20) and in TiKV ( ￿─ �→ StreamAgg_9) uses the stream aggregation, which is very efficient in its memory usage. The biggest issue with the current execution

Y Y Y Y TiDB Bin- log 7 Y Y Y Y Y Y Y Y Y Y Change data cap- ture (CDC) Y Y Y Y Y Y Y Y Y Y Stream data to Ama- zon S3, GCS, Azure Blob Stor- age, and NFS through TiCDC Y Y E N N N N N N N 7Starting different processing engines based on the specific business. • Real-time stream processing When using TiDB in real-time stream processing scenarios, TiDB ensures that all the data flowed in constantly in the URL. Then, resultset is automatically closed but the result set to be read in the previous stream- ing query is lost. • To use Cursor Fetch, first set FetchSize as a positive integer and configure

with TiCDC. After pausing data replication and conducting pre-online read-write tests in your down- stream TiDB cluster, this feature allows the cluster to gracefully and quickly roll back to the paused TSO race in some scenarios #49677 @lcwangchao • Fix the issue that query results are incorrect due to STREAM_AGG() incorrectly handling CI #49902 @wshwsh12 • Fix the issue that encoding fails when converting the issue that enforced sorting might become ineffective when a query uses optimizer hints (such as STREAM_AGG()) that enforce sorting and its execution plan contains IndexMerge #49605 @AilinKid • Fix the