This is the best current metric for frontend stalls, because the micro-op queue is responsible for most micro-ops feeding into the backend. Zen time tracking update#Tracking create time and last update time can now easily be accomplished with Zen v15 and the System Data v2 feature.“Op Queue Empty” shows how often the decoder or op-cache was unable to keep decoded micro-ops queued up in front of the renamer. So, a query with restrictions or sorting on the virtual columns will use the index when appropriate. The system data v2 indexes are fully optimizable by the SQL engine. Where Timestampdiff(SQL_TSI_hour, “sys$create”, now()) < 24 Select count(*) as Last24Count from sensorData –return the number of rows, inserted in the last 24 hours: Select sensorData.*, Timestampdiff(SQL_TSI_MINUTE,”sys$update”, now()) NumMinsįrom sensorData where “sys$update” > “sys$create” –return all CHANGED rows, including how many minutes since the last update Where “sys$update” > Timestampadd(SQL_TSI_MINUTE, -20, now()) –find rows inserted or updated in the last 20 minutes: Select “sys$create”, “sys$update”, sensorData.* from sensorData Update sensorData set temp = 90.1 where location = ‘Machine1’ This portion of the time stamp is used to guarantee uniqueness in the value, as opposed to representing the actual septaseconds of the insert.Īfter updating a row, you’ll see that only the sys$update value has changed: You’ll notice that the syskey data values show the fractional seconds as seven digits. Initially, the create time and the update time are recorded as the same value. Insert into sensorData values(‘Machine4’, 90.0) Insert into sensorData values(‘Machine3’, 65.4) Insert into sensorData values(‘Machine2’, 79.8) Insert into sensorData values(‘Machine1’, 77.3) Now, let’s insert a few rows and see what the virtual columns look like: Both cases will result in a 13.0 version file. This keyword can also be used in an ALTER TABLE statement to rebuild an existing file to include the new syskey values. To create a table including system data v2, add the “SYSDATA_KEY_2” keyword to the CREATE TABLE statement: Let’s look at an example executed in the Zen Control Center (The Zen Database Management Console): The data in these columns is stored as a Timestamp(7), which is a standard time stamp with septasecond granularity. In addition, system data v2 values can be accessed via any SQL interface using the virtual column names sys$create and sys$update. Like the original system data, the new hidden values can be retrieved via standard Btrieve methods by reading along key numbers 125 (insert time) and 124 (update time). The 13.0 file format is required for system data v2, and the rebuild utility can be used to enable this option on the files you select. will all cause the system data v2 time stamps to be maintained if the data file has this option enabled. So, applications written using Btrieve, Btrieve 2, ODBC, ADO.NET, PDAC, Java, etc. These time stamps are automatically handled by the engine for every insert and update received, regardless of the interface used. These values are actual time stamps which represent when the record was inserted into the file, and when it was last updated. Zen v15 introduces System Data v2, which provides two hidden unique values on every record. The hidden values can be retrieved via standard Btrieve Get operations by reading along key number 125 however, beyond being unique, the system data does not provide any additional information. Zen time tracking windows#It is also used by DataExchange (used for data replication between various instances of Zen Windows servers in distributed data environments) to uniquely identify records in files being replicated between systems. It is used in conjunction with transaction logging to provide data integrity and recovery in case of system failure. System data has been around for a long time it provides a hidden unique identifier on every record in a data file. With the release of Zen v15, there is now an easy way to do this for any existing Zen data file without impacting existing applications and data layouts – it’s called “System Data v2”. Most of these solutions often need database design changes or time-consuming processes to complete these tasks. Archiving historic data, synchronizing data after off-line access, or auditing changed data are all issues that typically require customized programming. Data maintenance is an ongoing requirement in every database environment.
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