SAP Fioiri sits on NetWeaver gateway and offeres out of box business rich process capabilities by leveraging your existing platform and mobilising through the use of browser not through mobile platform.
Here is a demonstration example: On your internet browser on phone or laptop open the link after proper installations. Then a link on the browser will prompt for a user to enter user id and password, the same authorisation you use in the SAP backend system. Depending on your profile you will be prompted for selecting profiles from 1 to It also coordinates and uses all the other servers.
This is used in a distributed system with instances of HANA database on different hosts. The name server knows where the components are running and which data is located on which server. Together, these functions provide robust security and data protection and enhanced data access. SAP HANA retains the ability to configure Connection and Session management parameters to accommodate complex security and data transfer policies instituted.
It also ensures that SQL statements are accurately authored and provides some error handling to make queries more efficient. The SQL processor contains several engines and processors that optimize query execution: This allows quick access to the most relevant data. This technology was further developed into a full relational column based store. This segmentation simplifies administration and troubleshooting. The algorithms and technology is based on concepts pioneered by MAX DB and ensures that the database is restored to the most recent committed state after a planned or unplanned restart.
Typically, these volumes are saved to media and shipped offsite for a cold-backup disaster recovery remedy. The Request Parser analyses the client request and dispatches it to the responsible component. The Execution Layer acts as the controller that invokes the different engines and routes intermediate results to the next execution step. For example, Transaction Control statements are forwarded to the Transaction Manager. Data Definition statements are dispatched to the Metadata Manager and Object invocations are forwarded to Object Store.
Data Manipulation statements are forwarded to the Optimizer which creates an Optimized Execution Plan that is subsequently forwarded to the execution layer. The motivation for SQLScript is to offload data-intensive application logic into the database. One such basic operation is to create a new version of a dataset as a copy of an existing one while applying filters and transformations. Planning data for a new year is created as a copy of the data from the previous year.
This requires filtering by year and updating the time dimension. Another example for a planning operation is the disaggregation operation that distributes target values from higher to lower aggregation levels based on a distribution function. The SAP HANA database also has built-in support for domain-specific models such as for financial planning and it offers scripting capabilities that allow application-specific calculations to run inside the database.
The Calculation Engine will break up a model, for example some SQL Script, into operations that can be processed in parallel. The engine also executes the user defined functions. New sessions are implicitly assigned to a new transaction. When a transaction is committed or rolled back, the transaction manager informs the involved engines about this event so they can execute necessary actions. The transaction manager also cooperates with the persistence layer to achieve atomic and durable transactions.
Metadata of all these types is stored in one common catalog for all SAP HANA database stores in-memory row store, in-memory column store, object store, disk-based. Metadata is stored in tables in row store. In distributed database systems central metadata is shared across servers. How metadata is actually stored and shared is hidden from the components that use the metadata manager.
A privilege grants the right to perform a specified operation such as create, update, select, execute, and so on on a specified object for example a table, view, SQLScript function, and so on. Analytic privileges grant access to values with a certain combination of dimension attributes. This is used to restrict access to a cube with some values of the dimensional attributes.
The database optimizer which will determine the best plan for accessing row or column stores. Optimised Write and Read operation is possible due to Storage separation i. Recent versions of changed records. Write Operations mainly go into Transactional Version Memory. Data that has been committed before any active transaction was started. It also clears outdated record versions from Transactional Version Memory. It can be considered as garbage collector for MVCC. Row store tables are linked list of memory pages.
Pages are grouped in segments. Typical Page size is 16 KB. Optimised Read and Write operation is possible due to Storage separation i. The update is performed by inserting a new entry into the delta storage. Even during the merge operation the columnar table will be still available for read and write operations. To fulfil this requirement, a second delta and main storage are used internally.
Engine uses multi version concurrency control MVCC to ensure consistent read operations. As row tables and columnar tables can be combined in one SQL statement, the corresponding engines must be able to consume intermediate results created by each other.
A main difference between the two engines is the way they process data: Row store operators process data in a row-at-a-time fashion using iterators. Column store operations require that the entire column is available in contiguous memory locations.
To exchange intermediate results, row store can provide results to column store materialized as complete rows in memory while column store can expose results using the iterator interface needed by row store. It ensures that the database is restored to the most recent committed state after a restart and that transactions are either completely executed or completely undone. To achieve this goal in an efficient way the per-sistence layer uses a combination of write-ahead logs, shadow paging and savepoints.
The persistence layer offers interfaces for writing and reading data. Log entries can be written implicitly by the persistence layer when data is written via the persistence interface or explicitly by using a log interface. So a typical Distributed Scale out Cluster Landscape will have many server instances in a cluster. Therefore Large tables can also be distributed across multiple servers. Again Queries can also be executed across servers.