Data dictionary in sql server
Author: s | 2025-04-24
I have to create a data dictionary in an existing database for all user tables within that database in SQL Server 2025. Does anyone have a good piece of sql to use for this purpose. Generating data dictionary for SQL Server database. 1. SQL Server- Table rows to data dictionary conversion. 1. SQL Database Data Dictionary Inquiry. 1. SQL SQL Server Data Dictionary Queries This is a list of handy SQL queries to the SQL Server data dictionary. You can also find 100 other useful queries here .
SQL Data Dictionary for SQL Server
ProblemThis tutorial will demonstrate how to migrate via JSON key-value pairs from a Python dictionary object to a SQL Server table. It will provide an in-depth review of how the key-value pairs are originally stored in Python, transformed to JSON, and exported to a file along with a use case example that reinforces how to import JSON data from a file and display the data within SQL Server.SolutionJSON (JavaScript Object Notation) is a popular open file format for exchanging data between applications as well as receiving data from a server. Because of the widespread adoption of JSON for exchanging data, it is increasingly important for SQL Server administrators to become familiar with how to retrieve and save JSON data for input to SQL Server tables. Also, SQL Server administrators may participate in projects for receiving content in JSON data files originating from other applications, such as Yahoo Finance. This tip presents a use case example for retrieving key-value paired data from Yahoo Finance with Python in order to export the key-value pairs to a file with a JSON data format for retrieval and saving in a SQL Server table.Summary of the use casePython is especially prized for its ability to interface with various application programming interfaces, such as those from Yahoo Finance for financial data and the National Oceanic and Atmospheric Administration for historical weather data. A recent MSSQLTips.com tip includes coverage of the yfinance library for use with Python. The yfinance library for Python can facilitate downloading historical price and volume data as well as fundamental data, such as number of employees, total public shares available for trading (float), and the number of shares shorted in a security. The fundamental data is available for each security via a Python dictionary object.The dictionary object is for storing ordered key-value pairs
davidurban7/Data-Dictionary-SQL: Data Dictionary for MS SQL Server
The Report Writer module enables you to produce ad hoc reports in the format you require. The menu driven design provides access to all system data with no programming skills required. Reports can be sent to the printer, screen preview or an ASCII file. Data can be read and written back to the database facilitating mass updates. Data from 3rd party sources can be used in reports. Data can also be exported to third party software such as spreadsheets, word processors, databases, etc. The Report Writer does not have any logic to physically create SQL tables (i.e. the table definition or the columns and their data types). The Report Writer cannot write any new rows to an existing SQL server table. It can only be used to update existing rows in a SQL table. Implementation The Report Writer module uses its own data dictionary and not the standard data dictionary. For this reason, before you can use the SYSPRO Report Writer, you must use the Data Dictionary Import Wizard program to create the Report Writer Data Dictionary from the standard dictionary supplied with SYSPRO. This applies irrespective of whether you are using a C-ISAM or a SQL Server-based system. The standard data dictionary contains all the table names, columns and table linkages required for your reports. The import program uses files in the Base/dd directory to create the data dictionary. Importing the Standard Data Dictionary does not have any effect on your data files. Even if you have a previous version of the Report Writer installed, we recommend that you re-import the standard data dictionary to ensure that you have the most up-to-date definition of tables and columns. It is advisable to re-import the data dictionary every time a new version of the software is installed. Once you have created the Report Writer data dictionary, you can optionally use: the Browse on Data Dictionary Tables program to amend the Report Writer data dictionary tables. You should never amend the standard data dictionary. the Data Dictionary Table Listing program to print the details of data dictionary tables. the Browse on Data Dictionary Columns program to create and maintain the Report Writer data dictionary column definitions. the Browse on Data Dictionary Linkages program to define how tables are logically linked. By linking a table, the columns available in the linked table can be used when creating a report. SQL vs C-ISAM TheData Dictionary SQL Server - docs.itrontotal.com
For Char columns, the Length and Decimals for Numeric columns. Datetime columns are automatically handled appropriately. When you define a numeric column such as decimal (14,2) then you define Length as 14 and 2 decimals with the '[x] SYSPRO Format number' option selected. All other fields are documentary. It is recommended that user-defined columns are named appropriately to avoid possible use by SYSPRO programs in the future. For example, you could start the Column name with 'Usr'. SYSPRO supports column names up to 18 characters in length. It is recommended that your columns are limited to char, decimal and datetime SQL Server datatypes as these are the types used by the standard SYSPRO system. In addition it is important that you define user-defined columns as nullable (NULL) or supply a suitable default to avoid problems when SYSPRO programs insert data into the row. The maximum size of anumeric field is 15 digits before the decimal and a maximum of 6 digits afterthe decimal (15.6). This number can be positive or negative. Defining user tables and accessing them in a report There are several steps to configure user tables to be accessed by a report on a SQL Server based system. Once these steps have been completed for each user table you can access the column from these tables in the report in exactly the same way as for standard SYSPRO columns. Create your user table and it's columns in the SYSPRO database where the rest of your SYSPRO data resides. SYSPRO supports table and column names up to 18 characters in length. It is recommended that your columns are limited to char, decimal and datetime SQL Server datatypes as these are the types used by the standard SYSPRO system. It is recommended that user-defined tables are named appropriately to avoid possible use by SYSPRO programs in the future. For example, you could start the Table name with 'Usr'. Typically populate this table with your data as applicable. Configure this table using the Browse on Data Dictionary Tables program. Typically you define the table name with the same name as the physical table defined in SQL Server. It is recommended that you assign an appropriate table description. Configure each column using the Browse on Data Dictionary Columns program. The name of the physical column in the table must be added to the data dictionary against the 'Column:' prompt and the Type. I have to create a data dictionary in an existing database for all user tables within that database in SQL Server 2025. Does anyone have a good piece of sql to use for this purpose. Generating data dictionary for SQL Server database. 1. SQL Server- Table rows to data dictionary conversion. 1. SQL Database Data Dictionary Inquiry. 1.Data Dictionary SQL Server - docs.itrontotaltest.com
Skip to main content This browser is no longer supported. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. sys.pdw_nodes_column_store_dictionaries (Transact-SQL) Article03/03/2023 In this article -->Applies to: Azure Synapse Analytics Analytics Platform System (PDW)Contains a row for each dictionary used in columnstore indexes. Dictionaries are used to encode some, but not all data types, therefore not all columns in a columnstore index have dictionaries. A dictionary can exist as a primary dictionary (for all segments) and possibly for other secondary dictionaries used for a subset of the column's segments.Column nameData typeDescriptionpartition_idbigintIndicates the partition ID. Is unique within a database.hobt_idbigintID of the heap or B-tree index (HoBT) for the table that has this columnstore index.column_idintID of the columnstore column.dictionary_idintId of the dictionary.versionintVersion of the dictionary format.typeintDictionary type: 1 - Hash dictionary containing int values 2 - Not used 3 - Hash dictionary containing string values 4 - Hash dictionary containing float valueslast_idintThe last data id in the dictionary.entry_countbigintNumber of entries in the dictionary.on_disk_sizebigintSize of dictionary in bytes.pdw_node_idintUnique identifier of a Azure Synapse Analytics node.PermissionsRequires VIEW SERVER STATE permission.See AlsoAzure Synapse Analytics and Parallel Data Warehouse Catalog ViewsCREATE COLUMNSTORE INDEX (Transact-SQL)sys.pdw_nodes_column_store_segments (Transact-SQL)sys.pdw_nodes_column_store_row_groups (Transact-SQL) --> Feedback Additional resources In this articleWhat is a SQL Server Data Dictionary? - Essential SQL
Try: sql = "drop table t1;" print ( sql ) stmt = IfxPy.exec_immediate(conn, sql) except: print ('FYI: drop table failed') i = 0 for sql in SetupSqlSet: i += 1 print (sql) stmt = IfxPy.exec_immediate(conn, sql) # The first record executed is for create table i -= 1 # Select records sql = "SELECT * FROM t1" stmt = IfxPy.exec_immediate(conn, sql) dictionary = IfxPy.fetch_both(stmt) rc = 0 while dictionary != False: rc += 1 print ("-- Record {0} --".format(rc)) print ("c1 is : ", dictionary[0]) print ("c2 is : ", dictionary[1]) print ("c3 is : ", dictionary["c3"]) print ("c4 is : ", dictionary[3]) print (" ") dictionary = IfxPy.fetch_both(stmt) print() print( "Total Record Inserted {}".format(i) ) print( "Total Record Selected {}".format(rc) ) # Free up memory used by result and then stmt too IfxPy.free_result(stmt) IfxPy.free_stmt (stmt) IfxPy.close(conn) print ("Done")####### Run the sample function ######my_Sample()Param Binding of Basic Data Types# Sample2.pyimport IfxPydef my_Sample(): ConStr = "SERVER=ids0;DATABASE=db1;HOST=127.0.0.1;SERVICE=9088;UID=informix;PWD=xxxxx;" try: # netstat -a | findstr 9088 conn = IfxPy.connect( ConStr, "", "") except Exception as e: print ('ERROR: Connect failed') print ( e ) quit() try: sql = "drop table t1;" print ( sql ) stmt = IfxPy.exec_immediate(conn, sql) except: print ('FYI: drop table failed') sql = "create table t1 ( c1 int, c2 char(20), c3 int, c4 int ) ;" stmt = IfxPy.exec_immediate(conn, sql) sql = "INSERT INTO t1 (c1, c2, c3, c4) VALUES ( ?, ?, ?, ? )" stmt = IfxPy.prepare(conn, sql) c1 = None c2 = None c3 = Nonesql server - Searching for a Data Dictionary for SQL or Oracle
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ProblemThis tutorial will demonstrate how to migrate via JSON key-value pairs from a Python dictionary object to a SQL Server table. It will provide an in-depth review of how the key-value pairs are originally stored in Python, transformed to JSON, and exported to a file along with a use case example that reinforces how to import JSON data from a file and display the data within SQL Server.SolutionJSON (JavaScript Object Notation) is a popular open file format for exchanging data between applications as well as receiving data from a server. Because of the widespread adoption of JSON for exchanging data, it is increasingly important for SQL Server administrators to become familiar with how to retrieve and save JSON data for input to SQL Server tables. Also, SQL Server administrators may participate in projects for receiving content in JSON data files originating from other applications, such as Yahoo Finance. This tip presents a use case example for retrieving key-value paired data from Yahoo Finance with Python in order to export the key-value pairs to a file with a JSON data format for retrieval and saving in a SQL Server table.Summary of the use casePython is especially prized for its ability to interface with various application programming interfaces, such as those from Yahoo Finance for financial data and the National Oceanic and Atmospheric Administration for historical weather data. A recent MSSQLTips.com tip includes coverage of the yfinance library for use with Python. The yfinance library for Python can facilitate downloading historical price and volume data as well as fundamental data, such as number of employees, total public shares available for trading (float), and the number of shares shorted in a security. The fundamental data is available for each security via a Python dictionary object.The dictionary object is for storing ordered key-value pairs
2025-03-30The Report Writer module enables you to produce ad hoc reports in the format you require. The menu driven design provides access to all system data with no programming skills required. Reports can be sent to the printer, screen preview or an ASCII file. Data can be read and written back to the database facilitating mass updates. Data from 3rd party sources can be used in reports. Data can also be exported to third party software such as spreadsheets, word processors, databases, etc. The Report Writer does not have any logic to physically create SQL tables (i.e. the table definition or the columns and their data types). The Report Writer cannot write any new rows to an existing SQL server table. It can only be used to update existing rows in a SQL table. Implementation The Report Writer module uses its own data dictionary and not the standard data dictionary. For this reason, before you can use the SYSPRO Report Writer, you must use the Data Dictionary Import Wizard program to create the Report Writer Data Dictionary from the standard dictionary supplied with SYSPRO. This applies irrespective of whether you are using a C-ISAM or a SQL Server-based system. The standard data dictionary contains all the table names, columns and table linkages required for your reports. The import program uses files in the Base/dd directory to create the data dictionary. Importing the Standard Data Dictionary does not have any effect on your data files. Even if you have a previous version of the Report Writer installed, we recommend that you re-import the standard data dictionary to ensure that you have the most up-to-date definition of tables and columns. It is advisable to re-import the data dictionary every time a new version of the software is installed. Once you have created the Report Writer data dictionary, you can optionally use: the Browse on Data Dictionary Tables program to amend the Report Writer data dictionary tables. You should never amend the standard data dictionary. the Data Dictionary Table Listing program to print the details of data dictionary tables. the Browse on Data Dictionary Columns program to create and maintain the Report Writer data dictionary column definitions. the Browse on Data Dictionary Linkages program to define how tables are logically linked. By linking a table, the columns available in the linked table can be used when creating a report. SQL vs C-ISAM The
2025-04-14Skip to main content This browser is no longer supported. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. sys.pdw_nodes_column_store_dictionaries (Transact-SQL) Article03/03/2023 In this article -->Applies to: Azure Synapse Analytics Analytics Platform System (PDW)Contains a row for each dictionary used in columnstore indexes. Dictionaries are used to encode some, but not all data types, therefore not all columns in a columnstore index have dictionaries. A dictionary can exist as a primary dictionary (for all segments) and possibly for other secondary dictionaries used for a subset of the column's segments.Column nameData typeDescriptionpartition_idbigintIndicates the partition ID. Is unique within a database.hobt_idbigintID of the heap or B-tree index (HoBT) for the table that has this columnstore index.column_idintID of the columnstore column.dictionary_idintId of the dictionary.versionintVersion of the dictionary format.typeintDictionary type: 1 - Hash dictionary containing int values 2 - Not used 3 - Hash dictionary containing string values 4 - Hash dictionary containing float valueslast_idintThe last data id in the dictionary.entry_countbigintNumber of entries in the dictionary.on_disk_sizebigintSize of dictionary in bytes.pdw_node_idintUnique identifier of a Azure Synapse Analytics node.PermissionsRequires VIEW SERVER STATE permission.See AlsoAzure Synapse Analytics and Parallel Data Warehouse Catalog ViewsCREATE COLUMNSTORE INDEX (Transact-SQL)sys.pdw_nodes_column_store_segments (Transact-SQL)sys.pdw_nodes_column_store_row_groups (Transact-SQL) --> Feedback Additional resources In this article
2025-04-19Try: sql = "drop table t1;" print ( sql ) stmt = IfxPy.exec_immediate(conn, sql) except: print ('FYI: drop table failed') i = 0 for sql in SetupSqlSet: i += 1 print (sql) stmt = IfxPy.exec_immediate(conn, sql) # The first record executed is for create table i -= 1 # Select records sql = "SELECT * FROM t1" stmt = IfxPy.exec_immediate(conn, sql) dictionary = IfxPy.fetch_both(stmt) rc = 0 while dictionary != False: rc += 1 print ("-- Record {0} --".format(rc)) print ("c1 is : ", dictionary[0]) print ("c2 is : ", dictionary[1]) print ("c3 is : ", dictionary["c3"]) print ("c4 is : ", dictionary[3]) print (" ") dictionary = IfxPy.fetch_both(stmt) print() print( "Total Record Inserted {}".format(i) ) print( "Total Record Selected {}".format(rc) ) # Free up memory used by result and then stmt too IfxPy.free_result(stmt) IfxPy.free_stmt (stmt) IfxPy.close(conn) print ("Done")####### Run the sample function ######my_Sample()Param Binding of Basic Data Types# Sample2.pyimport IfxPydef my_Sample(): ConStr = "SERVER=ids0;DATABASE=db1;HOST=127.0.0.1;SERVICE=9088;UID=informix;PWD=xxxxx;" try: # netstat -a | findstr 9088 conn = IfxPy.connect( ConStr, "", "") except Exception as e: print ('ERROR: Connect failed') print ( e ) quit() try: sql = "drop table t1;" print ( sql ) stmt = IfxPy.exec_immediate(conn, sql) except: print ('FYI: drop table failed') sql = "create table t1 ( c1 int, c2 char(20), c3 int, c4 int ) ;" stmt = IfxPy.exec_immediate(conn, sql) sql = "INSERT INTO t1 (c1, c2, c3, c4) VALUES ( ?, ?, ?, ? )" stmt = IfxPy.prepare(conn, sql) c1 = None c2 = None c3 = None
2025-04-20Data dictionary tables are protected and may only be accessed in debug builds of MySQL. However, MySQL supports access to data stored in data dictionary tables through INFORMATION_SCHEMA tables and SHOW statements. For an overview of the tables that comprise the data dictionary, see Data Dictionary Tables. MySQL system tables still exist in MySQL 8.4 and can be viewed by issuing a SHOW TABLES statement on the mysql system database. Generally, the difference between MySQL data dictionary tables and system tables is that data dictionary tables contain metadata required to execute SQL queries, whereas system tables contain auxiliary data such as time zone and help information. MySQL system tables and data dictionary tables also differ in how they are upgraded. The MySQL server manages data dictionary upgrades. See How the Data Dictionary is Upgraded. Upgrading MySQL system tables requires running the full MySQL upgrade procedure. See Section 3.4, “What the MySQL Upgrade Process Upgrades”.How the Data Dictionary is Upgraded New versions of MySQL may include changes to data dictionary table definitions. Such changes are present in newly installed versions of MySQL, but when performing an in-place upgrade of MySQL binaries, changes are applied when the MySQL server is restarted using the new binaries. At startup, the data dictionary version of the server is compared to the version information stored in the data dictionary to determine if data dictionary tables should be upgraded. If an upgrade is necessary and supported, the server creates data dictionary tables with updated definitions, copies persisted metadata to the new tables, atomically replaces the old tables with the new ones, and reinitializes the data dictionary. If an upgrade is not necessary, startup continues without updating the data dictionary tables. Upgrade of data dictionary tables is an atomic operation, which means that all of the data dictionary tables are upgraded as necessary or the operation fails. If the upgrade operation fails, server startup fails with an error. In this case, the old server binaries can be used with the old data directory to start the server. When the new server binaries are used again to start the server, the data dictionary upgrade is reattempted. Generally, after data dictionary tables are successfully upgraded, it is not possible to restart the server using the old server binaries. As a result, downgrading MySQL server binaries to a previous MySQL version is not supported after data dictionary tables are upgraded.Viewing Data
2025-04-18