Sap ha350 pdf download

From performance perspective, the Left Outer Join are almost equally fast as Referential Join, while the Inner Join is usually slower due to the fact, that the join is always executed.

If that is not the case then referential joins have the possibility to give incorrect calculations if the referential integrity is not met — meaning if a delivery header is created but the items is not processed until a later stage then any calculations that use referential joins will be incorrect. This means that if there is a 1:n relationship the number of records can be greater that the number of records in the left table. It includes all the rows from both of the tables or result sets participating in the Join.

Like an Inner Join when join is When field from both tables are executed requested an inner Join is performed. Union Connecting Tables Caution!! N input sources. This context is provided by text tables which give meaning to the master data. For example, if our fact table or analytic view only contains some numeric ID for elink in information about each dealer using an attribute view. As it is of little use to sum up attributes from master data tables there is no need to define measures or aggregates for Attribute Views.

Example :Two logical join of different join types but defined on the same attribute view. The only editable field will be its description. In according to that statement, Attribute Views are reusable objects. There are business needs where in users would like to derive attributes using available attributes and measures.

Once created will behave like any other attributes in the whole information modeling paradigm. The analytics will support information needs in accordance to the calendar that are defined for reporting using fiscal calendars.

Don't use the Text Join in the Analytical View! Its not supported and will be removed from the drop-down list box soon. In the context of an Analytical View this attribute will then not be visible. Obviously this source type is best suitable to take a single flat table as input whose rows correspond to hierarchy nodes and contain a node ID and a parent node ID. For example, the recursive source transformation is able to interpret a geographical hierarchy that is based on multiple independent tables representing hierarchy levels connected by foreign key relations, provided that the source data is suitably pre-transformed via SQL.

For example, organizational structures, and so on. The hierarchy can be explored based on a selected parent, and there are cases where the child can be a parent. DE US These Attributes can be anything in the base table or view that the modeler wants to define in order to help reporting or further modeling. You have a transactional table with cost data items, with each cost type split on a different line. You can set it restricted to multiple attributes depending on your reporting requirements.

There are also multiple operators to choose from. The aggregated granularity of for example Price does not mean anything. Multiplying these to aggregates will not give a meaningful result. They would never be able to understand so much information or consume it in some meaningful way. A set of data can be aggregated by a region, a date, or some other group in order to minimize the amount of data passed between views. Those are mainly used for client dependent configuration e.

Do not set filter on field level in the modeler. Newer versions of the modeler already have an entry called "dynamic" in the drop down list box. They are basically simple value lists with language dependent texts. As such, they can only contain the values available in the Attribute they relate to.

Therefore, a data type for the Input Parameter must be specified. You assign values to these variables by entering the value manually, or by selecting it from the Calculation Engine drop-down list. You select the Attribute in the view that you want to filter on, and you also define the: Selection Type: Whether selections should be based on intervals, ranges or single values.

Multiple Entries: Whether multiple values of the selection types should be allowed. You can also define whether the Variable is Mandatory or if it should have a Default Value. After a variable has been created it also needs to be assigned and applied to an Attribute as a Filter.

The correct Data Type will then automatically be assigned to the filter. You might want to take input from the user and process it, returning dynamic data based on the user selection. Input Parameters makes this possible. Date Use this to retrieve a date from the end user using a calendar type input box.

Static List Use this when the end user should have a set list of values to choose from. Attribute Value When an Input Variable has this type, it serves the same purpose as a normal Variable. The Input Parameter can be of any suitable type, for example a StaticList type. In for example a Calculated Measure, we can reference the result of the user selected Input Parameter. Whatever input is selected in the variable can be used as a basis for extended calculations.

Dates can also be selected as ranges. They can be used in the same way as analytic views, however, in contrast to analytic views it is possible to join several fact tables in a calculation view. It is also possible to include more advanced calculations in a calculation view. It should however be noted that it may not be as fast as an Analytical View.

This means that with the columns generated by the Calculation View, you can also create new Calculated Attributes specific to the Calculation View. This can be useful when it is required to necessary to create a list based view, in essence creating a complex Attribute View. For this functionality to be utilised the Calculation View can however only contain attributes, no measures.

Germany 17 A Simple Calculation View is not Italy 5 meant to aggregate measures, so a Italy 5 careful approach has to be taken when including values. When using the aggregation node you can specify which columns should be aggregated and also the aggregation type sum, min or max. You can also add Calculated Columns to the node.

These calculations will be performed after aggregation. In order for the columns from the different sources to go into the correct target, a In Graphical Calculation views mapping will need to be provided. This can be done via a drag and drop interface. You can then set a Constant Value for the source columns that do not have the target column. It is used to retrieve, store or manipulate information in the database. This style of commenting is used to place comments on multiple lines.

NOTE: Undelimited identifiers are implicitly treated as upper case. Quoting identifiers will respect capitalization and allow for using white spaces etc. A special value of NULL is included in every data type to indicate the absence of a value. A value, expression1, is tested for a pattern, expression2. LIKE returns true if the pattern specified by expression2 is found.

To match a percent sign or underscore in the LIKE predicate, an escape character must be used. Operators can be used for calculation, value comparison or to assign values. They are allowed anywhere an expression is allowed.

Functions use the same syntax conventions used by SQL statements. When strings with numeric characters are given as inputs, implicit conversion from string to number is performed automatically before computing the result values. Function Expressions SQL built-in functions can be used as an expression. Uses an aggregate function to calculate a single value from the values Aggregate Expressions of multiple rows in a column.

When used as an expression, a scalar subquery is allowed to return only zero or one value. Otherwise the expression following the ELSE statement is returned, if it exists. MIN Returns the minimum value of expression. MAX Returns the maximum value of expression.

SUM Returns the sum of expression. AVG Returns the arithmetical mean of expression. VAR Returns the variance of expression as the square of standard deviation. ROW-based storage is preferable, if the majority of ROW access involves selecting a few records with all attributes selected. The table is truncated at the end of the session. Data Extension Allows the definition of table types without corresponding tables.

Functional Extension Allows definitions of side-effect free functions which can be used to express and encapsulate complex data flows Procedural Extension Provides imperative constructs executed in the context of the database process.

The orchestration logic can also execute declarative logic that is defined in the functional extension by calling the corresponding procedures. The imperative extension refers to the superset of this functional core with statements that store and modify both local and global state using assignments. This logic is internally represented as data flows which can be executed in parallel. As a consequence, operations in a dataflow graph have to be free of side effects. This means they must not change any global state either in the database or in the application.

The first condition is ensured by only allowing changes on the dataset that is passed as input to the operator. The second condition is achieved by only allowing a limited subset of language features to express the logic of the operator.

These table types are used to define parameters for a procedure. These table types are used to define parameters for a procedure that represent tabular results.

In order to create a table type in a different schema than the current default schema, the schema has to be provided as a prefix e. The table type is specified using a list of attribute names and primitive data types. For each table type, attributes must have unique names. The query optimizer will decide if a materialization strategy which avoids re-computation of expressions or other optimizing rewrites are best to apply. In any case, it eases the task to detect common sub-expressions and improves the readability of the SQLScript code.

It is implemented using SQLScript. Default is SQLScript. It is good practice to define the language in all procedure definitions. Other implementation languages are supported but not covered here. In this case, privileges are checked at runtime with the privileges of the caller of the function.

Please note that analytical privileges are checked regardless of the security mode. This marks a procedure as being free of side- effects. One factor to be considered here is that neither DDL nor DML statements are allowed in its body, and only other read-only procedures can be called by the procedure.

The advantage to this definition is that certain optimizations are only available for read-only procedures. The name of the result view is no longer bound to a static name scheme but can be any valid SQL identifier. Syntax CALL [schema. CALL returns an iterator over result sets. Each output variable of the procedure will be represented as a result set.

SQL statements that are not assigned to any table variable in the procedure body will be added as result sets at the end of the result set iterator. The type of these result structures will be determined during compilation time but will not occur in the signature of the procedure. Scalar output variables will be a scalar value that can be retrieved from the callable statement directly. This is used to populate an existing table by passing it as parameter.

The Calc Engine calculation engine instantiates Model Optimizer rule based a calculation model at time of R query execution. Each node has a set of inputs and outputs and an operation that transforms the inputs into the outputs. The calculation performed by such a node is described using the R language for statistical computing. It takes the name of the table and returns its content bound to a variable.

Optionally a list of attribute names can be provided to restrict the output to the given attributes. In the case of relational operators, the attributes may be renamed in the projection list. The functions that provide data source access provide no renaming of attributes but just a simple projection. It takes the name of the OLAP view and an optional list of key figures and dimensions as parameters. It takes the name of the calculation view and optionally a projection list of attribute names to restrict the output to the given attributes.

This allows exploitation of the specific semantics of the calculation engine and to tune the code of a procedure if needed. One table variable representing the left argument to be joined. One table variable representing the right argument to be joined. A list of join attributes. The list must at least have one element. Optionally, a projection list can be provided specifying the attributes that should be in the resulting table.

If this list is present it must at least contain the join attributes. For each pair of join attributes, only one attribute will be in the result. Optionally, a projection list of attribute names can be given to restrict the output to the given attributes. If a projection list is provided, it must include the join attributes. Finally, the plan operator requires each pair of join attributes to have identical attribute names. In case of join attributes having different names, one of them must be renamed prior to the join.

Optionally renames columns, computes expressions, or applies a filter. As last step, the filter is applied. It evaluates an expression and is usually then bound to a new column. The expression enclosed in single quotes. The result type of the expression as a SQL type. A variable of type table containing the data that should be aggregated. A list of aggregates. An optional list of group-by attributes.

For instance, ["C"] specifies that the output should be grouped by column C, i. If this list is absent the entire input table should be treated as a single group, and the aggregate function is applied to all tuples. It computes the union of two tables which need to have identical schemas. If this expression evaluates to true then the statements — then-stmts1 — in the mandatory THEN block are executed. The remaining parts are optional.

In most cases this branch starts with ELSE. The statements — else-stmts3 — are executed without further checks. If it evaluates to true, the statements — then-stmts2 — is executed. Iteration starts with value start and is incremented by one until the loop-var is larger than end.

Hence, if start is larger than end, the body loop will not be evaluated. For each enumerated value of the loop variable the statements in the body of the loop are evaluated. It provides more flexibility in creating SQL statements. This statement allows for constructing an SQL statement at execution time of a procedure.

The graph is there for illustration, the actual currency conversion is not done graphically in a Calculation View. They must be replicated so that the conversions are working correctly. A Fixed Currency will convert the Source Currency into a single currency.

If we know the base Currency we can set it as Date of a Fixed Type. The Date Mapping defines the date when we want the currency conversion to occur, based on either a Fixed date, an Attribute or a Variable.

Using this method all lines for the measure in the Analytic View will be converted using the same currency. This way we achieve a one- to-one conversion.

Enable for decimal shifts This option is to be used when you want to shift the decimal separator to the appropriate place according to the currency exchange rate data available in the master data tables. It is explained in more detial in the next module. You might instead want the option to let the? We need to use the same definition for our variable.

The variable created can then be selected as the Currency Type. The method used to convert currencies differs from how it is done in Analytic Views. Rather than defining the conversion rules graphically as done in the Analytic Views, the definitions will need to be written by the modeler.

At the moment this is the only option available for this argument. Fuzzy Search functionality enables finding strings that match a pattern approximately rather than exactly , both finding approximate substring matches inside a given string and finding dictionary strings that match the pattern approximately. Text Analysis scripts provides additional possibilities of analysing the strings or large text columns.

KG' in 'Walldorf' as a possible duplicate. The text analysis is a set of Python based scripts, that can be installed and it can then extract entities such as persons, products, places, and more from documents and thus enrich the set of structured information in SAP HANA.

These additional attributes enable improved analytics and search. The text analysis provides a vast number of possible entity types and analysis rules for many industries in 20 languages, they provide a rich standard set of dictionaries and rules for identifying and extracting entities from any business text.

The standard covers common entities such as organizations, persons, countries, dates, measures, and many more. These data structures exist in the memory only, so no additional disk space is required. N You should enable the fast fuzzy search structures for all database columns that have a high load of fuzzy searches, D and for all database columns that are used in performance- critical queries, to get the best response times possible.

The additional data structures increase the total memory E footprint of the loaded table. Without the Fuzzy option the search will only return results that contain the exact phrase searched for. The higher the score, the more similar the strings are. A score of 1. A score of 0. You can sort the results of a query by score in descending order to get the best records first the best record is the record that is most similar to the user input.

When a fuzzy 1. The values of a column are compared with the user input, using the fault-tolerant fuzzy string comparison. Texts are tokenized split into terms and the fuzzy comparison is done term by term. KG' gets a high score, because the term 'SAP' exists in both texts.

If set to for example 0. Default is 0. You can also select several Information Objects. For example, when you create an attribute view for the first time, before activating it, the view active does not exist yet in the system. When you modify an existing view active, you create a new view inactive, but before activating it again, the previous view active is still available.

So, the system do not allow to have two different Views Inactive. This function could be very helpful to study the impacts of changes in the data model. Select an object, do a right click and select « Where Used » function. Thereby, « Type », « Name » and « Package » of each object which are currently used for the selected object are displayed.

These documents could provide a list of all objects containing in a package or details on previous selected objects. You can generate Auto Documentation with a right click on an Information Objects or directly with the button below. You can add objects from different packages in the same generated document. Then, choose an target emplacement to save the generated documents. For import you need to create the schema where all the tables are imported. Schemas are created with a SQL Script statement.

You can define different schema mappings in the same time. From the Quick Launch tab page, choose « Delivery Units.. You need to associate packages with delivery units. This is required when you export models. Enter the responsible user. In the Version field, enter the delivery unit version. Enter the support package version of the delivery unit.

Enter the patch version of the delivery unit. Prerequisites : You have created a delivery unit. This mode of export should only be used in exceptional cases, since this does not cover all aspects of an object, for example, translatable texts are not copied. A general best practice recommendation is to periodically schedule full exports, and have a few exports using Filter By Time in between.

Select the file repository on the server where models have been exported. Then select models you want to import. Only active objects can be exported in this mode. These will be exported to the server and the file s can then be sent to SAP support for troubleshooting purposes. Then define the folder location and select the package or models you want to export.

Procedure: 1. From the Quick Launch tab page, choose Mass Copy. Select the required object s. Choose Add. Choose Next. Copy checkbox. Choose Finish. The status of content copy can be viewed in Job log. In order to prepare for translation, some metadata that is used by the translation system must first be maintained in the package. This metadata maintenance is available from the Edit Package Details dialog. Enter a Text Collection to associate a package with a collection in order to specify the language into which the package objects are to be translated.

To provide a suggestion regarding the translation of the package, enter text in Hint. Enter a text status. Choose OK. Upload : Uploads the texts from the file system to the SAP translation system. After this step, the translators can translate the texts from the original language into the required target languages. Download: Downloads the translated texts from the SAP translation to the file system. A Role User user can be the owner of database objects. Privileges are required to model access control.

Roles can be used to structure the access control scheme and model reusable business roles. Roles can be nested so that role hierarchies can be implemented. This makes them very flexible, allowing very fine- and coarse-grained authorization management for individual users.

This means whenever a user tries to access an object, the system performs an authorization check using the user, the user's roles, and directly allocated privileges.

As soon as all requested privileges have been found, the system aborts the check and grants access. Some of them are templates that need to be customized; others can be used as they are. Once installation is complete, configure Kerberos as follows for more information, see the Kerberos product documentation : 1. For this user, create one service principal name SPN for each host of the system. Export each of the SPNs created into a separate file. Import the SPNs in each of the files to the respective host.

For this purpose, specify the user principal name UPN as the external ID when creating the database user. In the navigator, select Catalog Authorization. From the context menu, select New User. In the User Name field, enter the user name. SAML assertion is issued by the R identity provider after the client was Application Identity successfully authenticated there.

SAML is used to securely connect Internet applications that exist both inside and outside the organization's firewall. For users, Internet SSO eliminates additional logins to external resources. For system administrators, it improves security and reduces costs. Whenever the application srver needs to connect to the database on behalf of the user, it requests a SAML assertion from the client.

The assertion is then forwarded to the SAP HANA database, which will grant access based on the previously established trust to the identity provider. Concept Package Privilege Analytic Privilege Restrict the access to and the use of packages Package Analytic Analytic Privileges in the repository privilege privilege are used to provide row-level authorization Views. To be able to work with packages, the respective Package Privileges must be granted.

Following the principle of least privilege, users should only be given the smallest set of privileges required for their role. Two groups of SQL Privileges are available: System Privileges These are system-wide privileges that control some general system activities mainly for administrative purposes, such as creating schema, creating and changing users and roles. This privilege can only be granted on a schema.

This collection is dynamically evaluated for the given grantor and object. While the DROP privilege is valid for all kinds of objects, the ALTER privilege is not valid for sequences and synonyms as their definitions cannot be changed after creation. This privilege can only be applied to a schema, table, and table type. Normally, the content of those views is filtered based on the privileges of the user..

Normally, the content of those views is filtered based on the privileges of the accessing user. Because of the high level of impact on the system, these privileges are not designed for a normal database user. Caution must be taken when granting these privileges for example, only grant them to a support user or role. If you grant privileges to a user for a package, the user is automatically also authorized for all corresponding subpackages.

Native packages are packages that were created in the current system and should therefore, be edited in the current system. Imported packages from another system should not be edited, except by newly imported updates.

An imported package should only be manually edited in exceptional cases. READ : This privilege authorizes read access to packages and design-time objects, including both native and imported objects. They provide the ability for row- level authorization, based on the values in one or more columns. However, the different users may not be allowed to see the same data.

For example, different regional sales managers, who are only allowed to see sales data for their regions, could reuse the same Analytic View. They would get the Analytic Privilege to see only data for their region, and their queries on the same view would return the corresponding data. While the concept itself is very similar, SAP NetWeaver BW would forward an error message if you executed a query that would return values you are not authorized to see.

With the SAP HANA database, the query would be executed and, corresponding to your authorization, only values you are entitled to see returned. This may involve a single view, a list of views or, by means of a wildcard, all applicable views. This means that the activity READ is restricted and not available for use. These are applied to the actual attributes of a view.

Each dimension restriction is relevant for one dimension attribute, which can contain multiple value filters. Each value filter is a tuple of an operator and its operands, which is used to represent the logical filter condition. An Analytic Privilege is applicable to a view if it contains the view in the Cube restriction and contains at least one filter on one attribute of this view.

Attribute Views cannot be nested in other Attribute Views. The information modeler allows Analytic Views to be associated with Attribute Views to reuse the specified join paths.

However, it is not possible to use existing Attribute or Analytic Views as base views join candidates and use these as the basis for defining new Analytic Views. This introduces interdependencies between the views. Press the Diagnosis Files tab. Find the file index server. However, this is restricted by missing SQL Privileges on those activated objects. Only objects for which the users have access rights are visible. By default, this role is assigned to each user. Use this role as a template for what content administrators might need as privileges.

Reactivation of Users The administrator can reactivate a user account. If the user has made too many invalid logon attempts, the administrator can use an SQL command to unlock the user account. For example, if an employee temporarily leaves the company or if a security violation is detected. From the context menu of the user record, select Open. I found this informative and interesting blog so i think so its very useful and knowledge able.

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