However, sometimes actions can't be rolled back and it is unfortunately unavoidable. For example, consider when we send emails during the call to process. If we send before saving a record and that record fails to save what do we do? We can't unsend that email.

I prefer not to duplicate the name of the table in any of the columns (So I prefer option 1 above).

IANA Time Zone Database Main time zone database. This is where Moment TimeZone sources its data from.

Databases If databases data is stored on a ZFS filesystem, it’s better to create a separate dataset with several tweaks: zfs create -o recordsize=8K -o primarycache=metadata -o logbias=throughput -o mountpoint=/path/to/db_data rpool/db_data recordsize: match the typical RDBMSs page size (8 KiB) primarycache: disable ZFS data caching, as RDBMSs have their own logbias: essentially, disabled log-based writes, relying on the RDBMSs’ integrity measures (see detailed Oracle post)

Interaction with stable storage in the modern world isgenerally mediated by systems that fall roughly into oneof two categories: a filesystem or a database. Databasesassume as much as they can about the structure of thedata they store. The type of any given piece of datais known (e.g., an integer, an identifier, text, etc.), andthe relationships between data are well defined. Thedatabase is the all-knowing and exclusive arbiter of ac-cess to data.Unfortunately, if the user of the data wants more di-rect control over the data, a database is ill-suited. At thesame time, it is unwieldy to interact directly with stablestorage, so something light-weight in between a databaseand raw storage is needed. Filesystems have traditionallyplayed this role. They present a simple container abstrac-tion for data (a file) that is opaque to the system, and theyallow a simple organizational structure for those contain-ers (a hierarchical directory structure)

I've spent the last 3.5 years building a platform for "information applications". The key observation which prompted this was that hierarchical file systems didn't work well for organising information within an organisation.However, hierarchy itself is still incredibly valuable. People think in terms of hierarchies - it's just that they think in terms of multiple hierarchies and an item will almost always belong in more than one place in those hierarchies.If you allow users to describe items in the way which makes sense to them, and then search and browse by any of the terms they've used, then you've eliminated almost all the frustrations of a file system. In my experience of working with people building complex information applications, you need: * deep hierarchy for classifying things * shallow hierarchy for noting relationships (eg "parent company") * multi-values for every single field * controlled values (in our case by linking to other items wherever possible) Unfortunately, none of this stuff is done well by existing database systems. Which was annoying, because I had to write an object store.

Image a situation wherein you have just launched your app. But the data of your app is not being properly displayed or you are not able to fetch the data that is being entered by the users. What will be the impression of your app in the user’s mind?

I too have been confused by behavior like this. Perhaps a clearly defined way to isolate atomic units with synchronous reactivity would help those of us still working through the idiosyncrasies of reactivity.

The Realm is a new database module that is improving the way databases are used and also supports relationships between objects. If you are part of the SQL development world, then you must be familiar with the Realm.

So Memex was first and foremost an extension of human memory and the associative movements that the mind makes through information: a mechanical analogue to an already mechanical model of memory. Bush transferred this idea into information management; Memex was distinct from traditional forms of indexing not so much in its mechanism or content, but in the way it organised information based on association. The design did not spring from the ether, however; the first Memex design incorporates the technical architecture of the Rapid Selector and the methodology of the Analyzer — the machines Bush was assembling at the time.

In this writeup, we will be discussing one such important prerequisite that is React Native database. We can call it the backbone of React Native applications.

Allows batch updates by silencing notifications while the fn is running. Example: form.batch(() => { form.change('firstName', 'Erik') // listeners not notified form.change('lastName', 'Rasmussen') // listeners not notified }) // NOW all listeners notified

Normalize the database for this case if your data is going to be modified multiple times

normalizing our dabatase will help us. What means normalize? Well, it simply means to separate our information as much as we can

Denormalization is a database optimization technique in which we add redundant data to one or more tables

No need for DatabaseCleaner (rolling back transactions are usually faster than truncate).

Consider the following when picking technologies

Deadlocks are a classic problem in transactional databases, but they are not dangerous unless they are so frequent that you cannot run certain transactions at all. Normally, you must write your applications so that they are always prepared to re-issue a transaction if it gets rolled back because of a deadlock.

transaction calls can be nested. By default, this makes all database statements in the nested transaction block become part of the parent transaction. For example, the following behavior may be surprising: User.transaction do User.create(username: 'Kotori') User.transaction do User.create(username: 'Nemu') raise ActiveRecord::Rollback end end creates both “Kotori” and “Nemu”. Reason is the ActiveRecord::Rollback exception in the nested block does not issue a ROLLBACK. Since these exceptions are captured in transaction blocks, the parent block does not see it and the real transaction is committed.

One workaround is to begin a transaction on each class whose models you alter:

I think you should normalize if you feel that introducing update or insert anomalies can severely impact the accuracy or performance of your database application.  If not, then determine whether you can rely on the user to recognize and update the fields together. There are times when you’ll intentionally denormalize data.  If you need to present summarized or complied data to a user, and that data is very time consuming or resource intensive to create, it may make sense to maintain this data separately.

Can database normalization be taken too far?  You bet!  There are times when it isn’t worth the time and effort to fully normalize a database.  In our example you could argue to keep the database in second normal form, that the CustomerCity to CustomerPostalCode dependency isn’t a deal breaker.

Now each column in the customer table is dependent on the primary key.  Also, the columns don’t rely on one another for values.  Their only dependency is on the primary key.

A table is in third normal form if: A table is in 2nd normal form. It contains only columns that are non-transitively dependent on the primary key

From a narratological perspective, it would probably be fair to say that most databases are tragic. In their design, the configuration of their user interfaces, the selection of their contents, and the indexes that manage their workings, most databases are limited when set against the full scope of the field of information they seek to map and the knowledge of the people who created them. In creating a database, we fight against the constraints of the universe – the categories we use to sort out the world; the limitations of time and money and technology – and succumb to them.

columnar databases are well-suited for OLAP-like workloads (e.g., data warehouses) which typically involve highly complex queries over all data (possibly petabytes). However, some work must be done to write data into a columnar database. Transactions (INSERTs) must be separated into columns and compressed as they are stored, making it less suited for OLTP workloads. Row-oriented databases are well-suited for OLTP-like workloads which are more heavily loaded with interactive transactions. For example, retrieving all data from a single row is more efficient when that data is located in a single location (minimizing disk seeks), as in row-oriented architectures. However, column-oriented systems have been developed as hybrids capable of both OLTP and OLAP operations, with some of the OLTP constraints column-oriented systems face mediated using (amongst other qualities) in-memory data storage.[6] Column-oriented systems suitable for both OLAP and OLTP roles effectively reduce the total data footprint by removing the need for separate systems

seek time is incredibly long compared to the other bottlenecks in computers

Operations that retrieve all the data for a given object (the entire row) are slower. A row-based system can retrieve the row in a single disk read, whereas numerous disk operations to collect data from multiple columns are required from a columnar database.

Relational databases are designed around joins, and optimized to do them well. Unless you have a good reason not to use a normalized design, use a normalised design. jsonb and things like hstore are good for when you can't use a normalized data model, such as when the data model changes rapidly and is user defined. If you can model it relationally, model it relationally. If you can't, consider json etc.

Joins are not expensive. Who said it to you? As basically the whole concept of relational databases revolve around joins (from a practical point of view), these product are very good at joining. The normal way of thinking is starting with properly normalized structures and going into fancy denormalizations and similar stuff when the performance really needs it on the reading side. JSON(B) and hstore (and EAV) are good for data with unknown structure.

I chose all my scholarly journals, I put them together. I chose some YouTube videos; they were –IF:        Mm-hmm.CF:        – like, a bunch of TED talks.        

And – And I seen how – I saw how many, um, scholarly journals or how many sources came up for it, right? Um, number of sources. Right. And then, if I – if I felt like it wasn’t enough for me to thoroughly talk about the topic, I would move on. Right? So, when I did segregation, there – like, I guess, like, my specific topic was modern-day, so there wasn’t really much about it. Right? So, not much info. Right? And then, when I did gentrification, there were a lot, right?

The problem with the annotation notion is that it's the first time that we consider a piece of data which is not merely a projection of data already present in the message store: it is out-of-band data that needs to be stored somewhere.

many of the searches we want to do could be accomplished with a database that was nothing but a glorified set of hash tables

There are objects, sets of objects, and presentation tools. There is a presentation tool for each kind of object; and one for each kind of object set.

Massive mining of publicly available RNA-seq data from human and mouse

ATtRACT-a database of RNA-binding proteins and associated motifs

Find Open Speakers

AlleleDB is a repository, providing genomic annotation of cis-regulatory single nucleotide variants (SNVs) associated with allele-specific binding (ASB) and expression (ASE).

select top 1 * from newsletters where IsActive = 1 order by PublishDate desc

They have a very simplistic view of the activity being monitored by only distilling it down into only a few dimensions for the rule to interrogate

Football Leaks, which consists of 1.9 terabytes of information and some 18.6 million documents, ranging from player contracts to emails revealing secret transfer fees and wages, is the largest leak in the history of sport.