And there is so much to read, but so little time, right? Speed reading is something that has had a lot of attention in recent years. People are always looking for ways to get things done more efficiently. By learning to speed read you significantly process more information and get things done more quickly. However, with most speed reading techniques, you are required to adapt your natural reading style.
Most of them revolve eye coordination, reading between lines, skipping words, or visualizing while you are reading. I found that it was useful and productive at first, but after 1 or 2 days, I would fall back in my old patterns. While I am not questioning the effectiveness of these proven methods, I do think that they are not for everybody. These strategies are not focusing on the actual reading technique.
But rather on strategies that will improve the time you can finish a book or document. It is not necessarily about words per minute. It is finishing something in less time than before—but still capturing all information. We believe that we have to read every single word in a book, article, or document to understand it. We believe that we have to memorize everything because of pressure from school—we had to remember everything because we had exams. So it is time to get rid of a few misconceptions and hack your reading methods. Our brain can understand concepts without having to read a full book or article.
Understanding the outline will help you to skip non-essential parts. For example, start by preview the whole book you can also do this for rapports, blogposts, et cetera. Read the first few introductory paragraphs to grasp the message of the book. Next, read the subheading, titles, and subtitles.
Then, read the first and last sentence of each paragraph. This method will help you to comprehend the text faster. This strategy will give you 40 percent of the vital information. One bad habit is daydreaming—we think about the weirdest things when we are reading. Her experienced journalist voice is tempered with the creativity of a poet to help send her medicine out into a culturally divisive world through her book. Her retrospective stance is both attentive and productive.
More than vulnerable self-effacement, she enacts a grounded experiential. We witness many a meal and listen to a well-traveled storyteller who can spark a laugh as deft as she can reveal white supremacy. An astute and darkly witty voice that takes no prisoners and will hold you captive from the first page.
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Top 10 books that may take up your May — Loan Stars. The Space Between — 49th Shelf. New book discusses Canadian identity from an immigrant perspective — Richmond News. Whose land is it anyway? Amy Fung is a writer, researcher and curator born in Kowloon, Hong Kong, and spent her formative years in and around Edmonton on Treaty 6 Territory. Her writing has been published and commissioned by national and international publications, galleries, museums, festivals, and journals since Her multifarious curatorial projects have spanned exhibitions, cinematic and live presentations, as well as discursive events across Canada and abroad.
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Light Light by Julie Joosten. Due to the size of the database, syncing additional members, and creating additional backups, it took a significant amount of time which also delayed recovery. Hitting the connection limits caused node after node to drop offline, each of which had to be brought back to a healthy state. Each attempt to recover would cause multiple members of the replica set to go offline. We removed traffic in these cases to allow for recovery and incrementally added clients back to full operation to restore the service.
Performance Issues on a Subset of DB Nodes We had 2 specific members of our replica set that seemed to experience more severe performance degradation than others. When this occurred, logging stopped, and the Mongo process itself appeared completely hung. With no logging or ability to debug the system, we were left with an unknown root cause.
We have since isolated these nodes, replacing them with new members in the replica set. We also moved to MongoDB v3. Specific API Calls Reading From Primary We discovered a code path that had not been changed for some time that was pinning specific queries to the primary node.
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We made code changes during the outage to have these reads come from secondary members allowing us to share the workload across the replica set. All of the above changes, along with other specific tunings, allowed us to increase performance to the point of recovery and mitigate the circumstances that had resulted in cascading failures with our data store.
Improvements implemented since outage DB performance issues helped us discover a number of inefficiencies in data access patterns and updates.
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The Friend Suggestions feature on consoles had some scalability issues introduced in Fornite 3. Fortnite 3. Our longer-term goal though is to put the entire set of active users into a cache, so that even with DB in a degraded state actual impact would be much lower.
Yet another significant change we made was around the Fortnite client login sequence. Before our improvements, Fornite would reset the login sequence even if it failed well beyond the sign-in step.
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Now it tries its best to resume where possible, saving work. This will allow us to characterize disk problems in our fleet better and faster.
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Put in alarming on early indicators of DB cache exhaustion With the data from this event we are investigating appropriate early warning signs to alarm on cache pressure. Given different workloads and node types further analysis and load testing is required. Improve the speed of adding and replacing replica sets nodes One of the reasons recovery took longer than it might have was considerable amount of time it took to introduce new replica set nodes to replace unhealthy ones.
When you login to our Epic platform using 3rd party information we update our DB and we sync your display name you use in the game so that we can show it to other players. Finding a balance on the optimal syncing strategy, along with other opportunities to optimize DB access patterns is something we will continue to explore. Data set sharding Sharding data is a natural way of increasing overall service capacity by splitting data into well-isolated pieces and assigning tasks to dedicated processing resources hosting those pieces.
Sharding is most effective if you know in advance the shard to route your request to. Otherwise request needs to be handled by all shards in a cluster before you can get result. This is still better though than being capped with a single replica set. MongoDB uses shard keys to distribute data across shards. Every account document has a natural primary key, which would be an ideal candidate for the role of sharding key.