Wav2letter++, the fastest open source speech system, and flashlight

https://code.fb.com/ai-research/wav2letter/

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Large SAP tables which are good candidates for partitioning

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Not all tables are good candidates for partitioning. The tool we have developed runs through multiple checks for large tables to identify the right candidate. Generally if the answer is yes to the following questions, table partitioning may be a suitable strategy for improving performance. 1. Is the database large enough? 2. Is the table large enough? 3. Do your reports and queries have distinguishable access patterns? 4. Are you experiencing slowness and performance degradation? 5. Is the skew factor for the data distribution under reasonable limits? Below are some of the large tables which have been candidates for table partitioning amongst our SAP customers. Large material master tables MARA                   Material Master Table MARC                   Plant Material Data MARO...
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Partitioning in SAP HANA database

SAP HANA provides two ways of distributing data 1. Database Partitioning - different tables being distributed across several hosts (SAP Note 2044468 - FAQ:SAP HANA Partitioning) 2. Table Partitioning - splitting a column store table and distributing across several hosts (SAP Note 2081591 - FAQ: SAP HANA Table Distribution)  SAP HANA supports: 1. Hash Partitioning 2. Range Partitioning 3. Round-robin Partitioning In SAP HANA, we can create 1. Single-Level Partitioning 2. Multi-Level Partitioning The optimizing algorithm takes factors into account such as: - Balance tables equally in a way that they are spread over all the slave nodes and make optimal use of the available physical memory on each node - Ensure that tables/table partitions which are belonging to the same table groups (see tables _SYS_RT.TABLE_PLACEMENT, SYS.TABLE_GROUPS) are located on the same nodes, as they are logically frequently queried from application side via joins and if t...
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SAP HANA exploits partitioning in SAP BW for optimization

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Partitioning significantly improves query performance, based on the types of queries and on your hardware configuration as the system will find faster the requested information, because it will filter out all the irrelevant partitions and thus reduce the data volume to be read. This is a standard feature in most databases but SAP did not truly exploit this feature being database agnostic until HANA. In SAP/R3, SAP provided partition keys for BW but did not partition data during installation. With HANA the table partitioning/distribution is automated for SAP NetWeaver Business Warehouse. Database tables created for InfoCubes or classic DataStore objects are partitioned on the database. While the technical partitioning of data at database level helps optimizing the runtime of BW processes to a great extent, they alone are inadequate in building and maintaining data warehouses that scale easily with growing data volumes. Hence, to address some of the aspects of dealing with ...
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