Last month today: March in Google Cloud

While many of us had plans for March—including simply carrying out our normal routines—life as we know it has been upended by the global coronavirus pandemic. In a time of social distancing, technology has played a greater role in bringing us together. Here’s a look at stories from March that explored how cloud technology is helping and how it works under the hood to keep us connected.Technology in a time of uncertaintyThere are a lot of moving pieces, and a lot of dedicated technical people, who keep Google Cloud running every day, even when traffic spikes or unexpected events happen. Take a look at some of what’s involved with keeping systems running smoothly at Google, including SRE principles, longstanding disaster recovery testing, proprietary hardware, and built-in reserve capacity to ensure infrastructure performance. Plus, support agents are now provisioned for remote access, and an enhanced support structure is available for high-traffic industries during this time. You can dig deeper in this post on Google’s network infrastructure to learn how it is performing even under pressure. Google’s dedicated network is a global system of high-capacity fiber optic cables under both land and sea, and connects to last-mile providers to deliver data locally.Data plays a huge role in public health, and access to data sets and tools are essential for researchers, data scientists, and analysts responding to COVID-19. There’s now a hosted repository of related public datasets available to explore and analyze for free in BigQuery. These include the Johns Hopkins Center for Systems Science and Engineering, Global Health Data from the World Bank, and more.Working at home, together As work-from-home situations became a necessity globally in March, video conferencing and live streaming became even more essential for daily communication at work, school, and home. With that in mind, we announced free access to our advanced Meet capabilities to G Suite and G Suite for Education customers, including room for up to 250 participants per call, live streaming for up to 100,000 viewers within a domain, and the ability to record meetings and save them to Google Drive. Plus, we added Meet improvements for remote learning, and use of Google Meet surged to 25 times what it was in January, with day-over-day growth surpassing 60%. Technology is an essential aspect of working from home, but so is finding ways to collaborate with teammates and stay focused and productive amid distractions. Check out these eight tips for working from home for ways you can be proactive, organized, and engaged with work.Supporting those at-home workersIn this time of added network load and many people getting acquainted with working from home for the first time, the G Suite Meet team shared some best practices for IT admins to support their teams. These include tips on managing device policies, communicating effectively at scale, and use analytics to improve or change employee experiences. Plus, find some best practices that developers using G Suite APIs can follow to stay ahead of new user demands and onboarding. That’s a wrap for March.
Quelle: Google Cloud Platform

Improved database performance data: Key Visualizer now in Cloud Bigtable console

Cloud Bigtable is Google Cloud’s petabyte-scale NoSQL database service for demanding, data-driven workloads that need low latency, high throughput, and scale insurance. If you’ve been looking for more ways to monitor your Bigtable performance more easily, you’re in luck: Key Visualizer is now directly integrated into the Bigtable console. No need to switch to a Cloud Monitoring dashboard to see this data; you can now view your data usage patterns at scale in the same Bigtable experience. Best of all, we’re lowering the eligibility requirements for Key Visualizer usage, making it easier for customers to use this tool.If you aren’t yet familiar with Key Visualizer, it generates visual reports for your tables based on the row keys that you access. It’s especially helpful for iterating on the early designs of a schema before going to production. You can also troubleshoot performance issues, find hotspots, and get a holistic understanding of how you access the data that you store in Bigtable. Key Visualizer uses heatmaps to help you easily determine whether your reads or writes are creating hotspots on specific rows, find rows that contain too much data, or see whether your access patterns are balanced across all of the rows in a table. Here’s how the integration looks:Beyond bringing Key Visualizer into Bigtable, there are several other improvements to highlight: Fresher data. Where Key Visualizer used to serve data that was anywhere from seven to 70 minutes old, Key Visualizer in Bigtable can now show data that is approximately between four and 30 minutes old. To do that, Bigtable scans the data every quarter of the hour (10:00, 10:15, 10:30, 10:45), and then takes a few minutes to analyze and process that performance data.Better eligibility. We dropped the requirement on the number of reads or writes per second in order to make the eligibility criteria to scan data simpler: Now, you just need at least 30 GB of data in your table. This will lower the barrier for developers who want to fine-tune their data schema. Time range. It’s now easier to select the time range of interest with a sliding time range selector. Performance data will be retained for 14 days.The new version of Key Visualizer is available at no additional charge to Bigtable customers, and does not cause any additional stress on your application. If you’re ready to dig in, head over to Bigtable and choose “Key Visualizer” in the left navigation.For more ideas on how Key Visualizer can help you visualize and optimize your analytics data, read more about Key Visualizer in our user guide, or check out this brief overview video and this presentation on how Twitter uses Bigtable.
Quelle: Google Cloud Platform

Same Cloud Bigtable database, now for smaller workloads

Cloud Bigtable is a fast, petabyte-scale NoSQL database service that has long supported massive workloads, both internally at Google and for Google Cloud customers. We are now announcing that Bigtable is expanding its support for smaller workloads.You can now create production instances with one or two nodes per cluster, down from the previous minimum of three nodes per cluster. We are also expanding our SLA to cover all Bigtable instances, regardless of type or size. This means that you can get started for as low as $0.65/hour to take advantage of Cloud Bigtable’s low-latency data access and seamless scalability. Cloud Bigtable performs exceptionally well for use cases like personalization, fraud detection, time series, and other workloads where performance and scalability are critical. Bigtable at any scaleYou don’t need a terabyte- or petabyte-scale workload to take advantage of Bigtable! We want Bigtable to be an excellent home for all of your key-value and wide-column use-cases, both large and small. That’s true whether you’re a developer just getting started, or an established enterprise looking for a landing place for your self-managed HBase or Cassandra clusters.Get started by creating a new Bigtable instance:Making replication more affordableWe’ve seen customers use replication to get better workload isolation, higher availability, and faster local access for global applications. By reducing our minimum cluster size, it’s now more affordable than ever to try replication. To enable replication, just add a new cluster to any existing instance.Easy management of development and staging environmentsFinally, we heard your feedback that development instances were missing features needed to more easily manage development and staging environments. We’re excited to offer one-node production instances at the same price point as development instances, but with the added ability to scale up and down to run tests. You can now upgrade your existing development instances to a one-node production instance at any time.Learn moreTo get started with Bigtable, create an instance or try it out with a Bigtable Qwiklab. Between now and April 30, 2020, Google Cloud is offering free access to training and certification, including access to Qwiklabs, for 30 days. Register before April 30, 2020 to get started for free.
Quelle: Google Cloud Platform

Introducing incremental enrichment in Azure Cognitive Search

Incremental enrichment is a new feature of Azure Cognitive Search that brings a declarative approach to indexing your data. When incremental enrichment is turned on, document enrichment is performed at the least cost, even as your skills continue to evolve. Indexers in Azure Cognitive Search add documents to your search index from a data source. Indexers track updates to the documents in your data sources and update the index with the new or updated documents from the data source.

Incremental enrichment is a new feature that extends change tracking from document changes in the data source to all aspects of the enrichment pipeline. With incremental enrichment, the indexer will drive your documents to eventual consistency with your data source, the current version of your skillset, and the indexer.

Indexers have a few key characteristics:

Data source specific.
State aware.
Can be configured to drive eventual consistency between your data source and index.

In the past, editing your skillset by adding, deleting, or updating skills left you with a sub-optimal choice. Either rerun all the skills on the entire corpus, essentially a reset on your indexer, or tolerate version drift where documents in your index are enriched with different versions of your skillset.

With the latest update to the preview release of the API, the indexer state management is being expanded from only the data source and indexer field mappings to also include the skillset, output field mappings knowledge store, and projections.

Incremental enrichment vastly improves the efficiency of your enrichment pipeline. It eliminates the choice of accepting the potentially large cost of re-enriching the entire corpus of documents when a skill is added or updated, or dealing with the version drift where documents created/updated with different versions of the skillset and are very different in shape and/or quality of enrichments.

Indexers now track and respond to changes across your enrichment pipeline by determining which skills have changed and selectively execute only the updated skills and any downstream or dependent skills when invoked. By configuring incremental enrichment, you will be able to ensure that all documents in your index are always processed with the most current version of your enrichment pipeline, all while performing the least amount of work required. Incremental enrichment also gives you the granular controls to deal with scenarios where you want full control over determining how a change is handled.

Indexer cache

Incremental indexing is made possible with the addition of an indexer cache to the enrichment pipeline. The indexer caches the results from each skill for every document. When a data source needs to be re-indexed due to a skillset update (new or updated skill), each of the previously enriched documents is read from the cache and only the affected skills, changed and downstream of the changes are re-run. The updated results are written to the cache, the document is updated in the index and optionally, the knowledge store. Physically, the cache is a storage account. All indexes within a search service may share the same storage account for the indexer cache. Each indexer is assigned a unique cache id that is immutable.

Granular controls over indexing

Incremental enrichment provides a host of granular controls from ensuring the indexer is performing the highest priority task first to overriding the change detection.

Change detection override: Incremental enrichment gives you granular control over all aspects of the enrichment pipeline. This allows you to deal with situations where a change might have unintended consequences. For example, editing a skillset and updating the URL for a custom skill will result in the indexer invalidating the cached results for that skill. If you are only moving the endpoint to a different virtual machine (VM) or redeploying your skill with a new access key, you really don’t want any existing documents reprocessed.

To ensure that that the indexer only performs enrichments you explicitly require, updates to the skillset can optionally set disableCacheReprocessingChangeDetection query string parameter to true. When set, this parameter will ensure that only updates to the skillset are committed and the change is not evaluated for effects on the existing corpus.

Cache invalidation: The converse of that scenario is one where you may deploy a new version of a custom skill, nothing within the enrichment pipeline changes, but you need a specific skill invalidated and all affected documents re-processed to reflect the benefits of an updated model. In these instances, you can call the invalidate skills operation on the skillset. The reset skills API accepts a POST request with the list of skill outputs in the cache that should be invalidated. For more information on the reset skills API, see the documentation.

Updates to existing APIs

Introducing incremental enrichment will result in an update to some existing APIs.

Indexers

Indexers will now expose a new property:

Cache

StorageAccountConnectionString: The connection string to the storage account that will be used to cache the intermediate results.
CacheId: The cacheId is the identifier of the container within the annotationCache storage account that is used as the cache for this indexer. This cache is unique to this indexer and if the indexer is deleted and recreated with the same name, the cacheid will be regenerated. The cacheId cannot be set, it is always generated by the service.
EnableReprocessing: Set to true by default, when set to false, documents will continue to be written to the cache, but no existing documents will be reprocessed based on the cache data.

Indexers will also support a new querystring parameter:

ignoreResetRequirement set to true allows the commit to go through, without triggering a reset condition.

Skillsets

Skillsets will not support any new operations, but will support new querystring parameter:

disableCacheReprocessingChangeDetection set to true when you want no updates to on existing documents based on the current action.

Datasources

Datasources will not support any new operations, but will support new querystring parameter:

ignoreResetRequirement set to true allows the commit to go through without triggering a reset condition.

Best practices

The recommended approach to using incremental enrichment is to configure the cache property on a new indexer or reset an existing indexer and set the cache property. Use the ignoreResetRequirement sparingly as it could lead to unintended inconsistency in your data that will not be detected easily.

Takeaways

Incremental enrichment is a powerful feature that allows you to declaratively ensure that your data from the datasource is always consistent with the data in your search index or knowledge store. As your skills, skillsets, or enrichments evolve the enrichment pipeline will ensure the least possible work is performed to drive your documents to eventual consistency.

Next steps

Get started with incremental enrichment by adding a cache to an existing indexer or add the cache when defining a new indexer.
Quelle: Azure

Import Your WordPress Site to WordPress.com — Including Themes and Plugins

It’s been possible to export your posts, images, and other content to an export file, and then transfer this content into another WordPress site since the early days of WordPress.

Select WordPress from the list of options to import your site.

This basic WordPress import moved content, but didn’t include other important stuff like themes, plugins, users, or settings. Your imported site would have the same pages, posts, and images (great!) but look and work very differently from the way you or your users expect (less great).

There’s a reason that was written in the past tense: WordPress.com customers can now copy over everything from a self-hosted WordPress site — including themes and plugins — and create a carbon copy on WordPress.com. You’ll be able to enjoy all the features of your existing site, plus the the benefits of our fast, secure hosting with tons of features, and our world-class customer service.

Select “Everything” to import your entire WordPress site to WordPress.com.

To prep for your import, sign up for a WordPress.com account — if you’d like to import themes and plugins, be sure to select the Business or eCommerce plan — and install Jetpack (for free) on your self-hosted site to link it to WordPress.com. To start the actual import, head to Tools > Import in your WordPress.com dashboard.

Then sit back and relax while we take care of moving your old site to a new sunny spot at WordPress.com. We’ll let you know when it’s ready to roll!
Quelle: RedHat Stack