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Blind Write in DBMS

The role of the blind in Database Management Systems:

A blind write is an operation when a java developer or application does not check for the outcome of the write first before putting it through. Writing blind might result in cases when records are overwritten or different values are encountered because of conflicting unique constraints, foreign keys, or some other database protection. A privileged mode of blind writes without proper error handling can possibly get a data corrupted or lead to other unintentional consequences.

What Are Blind Writes?

Unlike a sequential read-and-write, a blind write is an operation that updates the database without seeking out current data by doing the read operation first. For example, the application would send an UPDATE or INSERT statement to the database, and replaces the value that already exists.

This technique could be termed blind ware because it has to do with researching without accessing the existing information. Application is unaware of what it is doing. It acts blindly while changing the table or the row.

Why Use Blind Writes?

There are a couple primary reasons why an application developer might to blind write operations:

  1. Performance Achievement- Every write before a read is an additional delay. Raw data may be modified with blind write command, skipping the read step, which will increase operation speed.
  2. Memory coherence- This situation arises when systems contend, and the data read can change before the write, which is referred to as lost updates and incorrect results. These Blind Writes can be avoided or mitigated by using this approach.

For instance, multithreaded process can implement using incremental counters. Doing that increment operation on every single client without reading the previous value, there is no chance of lost increments when other clients are trying to update it at the similar time.

Why Blind Writes Occur?

There are a few common reasons why developers end up performing blind writes:

  1. Laziness- Errors are checked during runtime and it takes a lot to find where an error occurred, hence, some developers skip error handling and checking for time savings. This is followed by the blatant plagiarism because of someone believing that there is no need to bother.
  2. Assumptions - A developer may simply assume that a write will be successful, thus they may not want to verify the constraints before it. However, if their predictions prove to be fallacious or the circumstances are no longer the same as they envisaged, the blind write could fail.
  3. Race Conditions- One of the most popular issues while designing concurrent applications is an issue of a so-called race condition. In this case a the developer can check if a write will succeed, then it may be determined that this write is safe, but by the time when the actual write occurs a split second later, conditions may have changed and the write can now fail because another operation occurred in between the check and write. In this case, unplanned black out can happen.

The Risks and Downsides

However, blind writes also come with significant downsides:

  • Writing over the critical data - Citing example, existing application data can be erased or corrupted without its knowledge. The modus operand has no idea where the existing status is.
  • Duplicate entries - During data insertion the database can unintentionally end up with duplicates or primary key contravention while it is not aware of that.
  • Referential integrity issues - It could happen that data is inserted or updated in a way that affects other tables that depend on it. This may end up to misbalanced and orphaned data which are not useful. One of the main problems which can occur when working with foreign key relationships is that they can be easily corrupted.
  • Bugs arise due to the race conditions - The same code that operated well in the testing environment may not have worked properly in production when it was run under heavy multi-user load.

Eight Top Manual Practices for a Blind Writer

But based on these hazards, one should avoid the blind write operations as much as possible. Some tips:

  • Implement automatic and healing steps to be carried out periodically, which will be aiming to clean the existing duplicates or orphan records.
  • When all possible, utilize an index to speed up search and apply primary and foreign key settings to reduce the chance to damage the database.
  • Blind writes overtake in case idempotence of updates or their repeatability and ordering do not influence the database's state.
  • Have admin alerts and logging that may help detect issues with blind writes that might escalate to violations of integrity.
  • Add in features like version numbers/timestamps when doing blind updates, this will allow applications be alerted of missing updates or concurrency conflicts as confirmed by applications rather than silently updating.

Fundamentally, lossless compression undermines data integrity for better speed. They are other NoSQL databases like them in which they value throughput more than consistency. To decide on whether or not if write blind technique usage is critical, we need to assess the application's parallelism patterns, the performance needs, and the tolerance for the data errors that last only momentarily. As read-before-write is more costly than write blind technique, engineering teams should only opt for read-before-write where the data errors cannot survive for long. Through the appropriate combination of the techniques, we can enable the production of more resilient and robust systems, which have the potential to keep data from wide spread loss or corruption.


As blind writes is one of the most important trade-off for database system design, it is the choice of obtaining availability and performance over weakly consistent write operations. The broker instead bids on the data without having any knowledge of results, thus enhancing their throughput and concurrency levels whilst relying on application level mechanisms to handle aborted write transactions. It lets the data processing engines to competently work with millions of processing and modifying data sets simultaneously, thereby pushing the complexity of the system out to the application level. Despite the fact that there are a lot of alterative concurrency methods available, the majority of the most popular traditional databases are still based on the blind writes scheme.

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