Update map job

Configuration Manager synchronizes with the Microsoft cloud service to get updates that apply to your infrastructure and version. You can install these updates from within the Configuration Manager console.

To view and manage the updates, make sure that you have the required permissions. For more information, see Install in-console updates for Configuration Manager. The service connection point is responsible for downloading updates that apply to your Configuration Manager infrastructure. In online mode, it automatically checks for updates every 24 hours. And it downloads available new updates for your current infrastructure and product version to make them available in the Configuration Manager console.

When your service connection point is in offline mode, use the service connection tool to manually sync with the Microsoft cloud. The following steps explain the flow in which an online service connection point downloads in-console updates:.

The manifest identifies whether there's a new update or hotfix available for download. The following entries are logged in DMPDownloader. Download manifest. Signing root cert's thumbprint: cdd4eeaeac7f40ccec Finished calling verify manifest Manifest. It triggers Hman to start processing, as follows:. Hman checks for the download signature, extracts the manifest, and then processes the manifest and checks applicability of the packages.

The following entries are logged in Hman. CAB' is signed and trusted. When you enable SQL logging, you can see each query run against the database. To run this process manually, follow these steps:. After the file is extracted, you can see all the update GUIDs of every update that has been released so far. Each GUID is unique. This folder contains SQL queries to run against the site server database to determine which update is applicable and which one is installed. The value of State shows the current state of the package.

If the update is applicable, DMPdownloader downloads the payload and redistributable files by using Setupdl. The following entries are logged:. INFO: setupdl. Connect without proxy. After the update is successfully downloaded, the following entries are logged in ConfigMgrSetup. For example:. The file displacement manager FDM moves the. This notification file marks that the update package is available to be installed.

If you haven't configured your hierarchy to have a Microsoft Intune subscription, the following entry is logged in Hman. CMU with no intune subscription. The Configuration Manager Admin console shows applicable updates as available. The following state types show an update as available within the console:. This folder contains the actual installation files for an update.

There's no Setup. Instead, an Install. This folder contains the latest client installation files. Active 9 months ago. Viewed 12k times. Improve this question. Kavya shree Kavya shree 1 1 gold badge 3 3 silver badges 19 19 bronze badges.

Add a comment. Active Oldest Votes. Example: TIME ' Improve this answer. However, it is a little bit hard to implement. Here is a complete shell script to add new file to configmap or replace existing one based on Bruce S. Here are the steps I have followed.. Note : In my case, the namespace is "test-namespace" and the configmap name is "test-nginx-ingress-controller". Note : Please replace the namespace and configmap name as per finding in the step 1.

As you can see below ignore all white placeholders , when your cluster's context is set on terminal you just type k9s and you will reach a nice terminal where you can inspect all cluster resources. Just type ":" and enter the resource name configmaps in our case which will appear in the middle of screen green rectangle.

Then you can choose the relevant configmap with the up and down arrows and type e to edit it see green arrow. For all Configmaps in all namespaces you choose 0 , for a specific namespace you choose the number from the upper left menu - for example 1 for kube-system:.

Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. Kubectl update configMap Ask Question. Asked 3 years, 8 months ago. Active 3 days ago. Viewed 84k times. I am using the following command to create a configMap. Option1 :- kubectl edit test Here, it opens the entire file. Note :- I dont want to have mongo.

Improve this question. Its answered here stackoverflow. A merge operation can produce incorrect results if the source dataset is non-deterministic. This is because merge may perform two scans of the source dataset and if the data produced by the two scans are different, the final changes made to the table can be incorrect.

Non-determinism in the source can arise in many ways. Some of them are as follows: Reading from non-Delta tables. For example, reading from a CSV table where the underlying files can change between the multiple scans.

Using non-deterministic operations. For example, Dataset. Schema validation merge automatically validates that the schema of the data generated by insert and update expressions are compatible with the schema of the table. It uses the following rules to determine whether the merge operation is compatible: For update and insert actions, the specified target columns must exist in the target Delta table. For updateAll and insertAll actions, the source dataset must have all the columns of the target Delta table.

The source dataset can have extra columns and they are ignored. For all actions, if the data type generated by the expressions producing the target columns are different from the corresponding columns in the target Delta table, merge tries to cast them to the types in the table.

Automatic schema evolution Note Schema evolution in merge is available in Databricks Runtime 6. See the examples below. Note This feature is available in Databricks Runtime 9.

Performance tuning You can reduce the time taken by merge using the following approaches: Reduce the search space for matches : By default, the merge operation searches the entire Delta table to find matches in the source table.

Adding the condition events. Merge examples Here are a few examples on how to use merge in different scenarios. Data deduplication when writing into Delta tables A common ETL use case is to collect logs into Delta table by appending them to a table. Note The dataset containing the new logs needs to be deduplicated within itself. Note Insert-only merge is optimized to only append data in Databricks Runtime 6. Write change data into a Delta table Similar to SCD, another common use case, often called change data capture CDC , is to apply all data changes generated from an external database into a Delta table.

Upsert from streaming queries using foreachBatch You can use a combination of merge and foreachBatch see foreachbatch for more information to write complex upserts from a streaming query into a Delta table.

Write a stream of database changes into a Delta table : The merge query for writing change data can be used in foreachBatch to continuously apply a stream of changes to a Delta table. Write a stream data into Delta table with deduplication : The insert-only merge query for deduplication can be used in foreachBatch to continuously write data with duplicates to a Delta table with automatic deduplication.

Note Make sure that your merge statement inside foreachBatch is idempotent as restarts of the streaming query can apply the operation on the same batch of data multiple times. When merge is used in foreachBatch , the input data rate of the streaming query reported through StreamingQueryProgress and visible in the notebook rate graph may be reported as a multiple of the actual rate at which data is generated at the source. This is because merge reads the input data multiple times causing the input metrics to be multiplied.

If this is a bottleneck, you can cache the batch DataFrame before merge and then uncache it after merge. Write streaming aggregates in update mode using merge and foreachBatch notebook Open notebook in new tab Copy link for import. Target columns: key, value Source columns: key, value, newValue. The table schema is changed to key, value, newValue. Target columns: key, oldValue Source columns: key, newValue.