Is there a guaranteed hash for every block? [duplicate]

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I am working in a computational research lab, and we're taking a look at Bitcoin. I'm trying to develop a simulation of Bitcoin mining for a nonexistent computer architecture.

I want to simulate the mining of a single block (say... the Satoshi block) without actually doing the work. To my understanding, each block in the chain has the winning nonce that enabled the miner to mine the block, but I also want to enumerate:

all of the losing hashes other potential winners that could have worked

Part of our simulation is the mathematics of our simulation (and the hypothetical architecture) could operate on all possible hashes at once.

A 256 bit hash for all 2^32 nonce values[1] of a block is only 2^(8+32) bits, or about 137.4 gigabytes.

I know this sounds like a silly question, but is there some place to download all 137.4 gigabytes of hashes for the Satoshi block or some other block? Otherwise, what is the best way to generate them? Can I pick up an old ASIC...

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This question already has an answer here:

disclaimer: I'm completely new at this, basic computer skills limited to GUI point-&-click

I took a look at how difficulty was explained, that it is "increased" by requiring the SHA-256 generation of blocks with more leading 0 bits, thus a string of 0s at the start of the hash which is more and more rare. Unless this has not been explained in a way I understand properly I think this means that you can cheat the Bitcoin difficulty and make blocks whenever you want? You simply need a custom SHA-256 hash generation program which non-randomly generates the specified number of 0s followed by a random SHA-256 hash shortened by the number of digits equal to the number of 0s in front, or simply generates a SHA-256 hash and then replaces the leading bits with 0 bits to meet the difficulty target thus forging a block hash. So from what I'm told it's A) easy to cheat; OR B) not well enough explained.

Isn't higher difficulty less...

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Spot the duplicate duck

Storage for DBAs: Data deduplication – or “dedupe” – is a technology which falls under the umbrella of data reduction, i.e. reducing the amount of capacity required to store data. In very simple terms it involves looking for repeating patterns and replacing them with a marker: as long as the marker requires less space than the pattern it replaces, you have achieved a reduction in capacity. Deduplication can happen anywhere: on storage, in memory, over networks, even in database design – for example, the standard database star or snowflake schema. However, in this article we’re going to stick to talking about dedupe on storage, because this is where I believe there is a myth that needs debunking: databases are not a great use case for dedupe.

Deduplication Basics: Inline or Post-Process

If you are using data deduplication either through a storage platform or via software on the host layer, you have two basic choices: you can deduplicate...

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The block header is the hash returned from generating a Merlkle tree that is below the current difficulty target for the blocks data.

Whats a hash?

In order to understand what the block header is you need to understand what a hashing function is. A hashing function is a one way(non invertable function) that maps a set of inputs to a set of outputs hash(s) -> p, where for our purposes s and p are both strings. For any string s we can find the hash by applying our hashing function, which will return a new string. This is a deterministic procedure in that given an s, the same hash() will produce the same p. There is no inverse of the hash operation, so you cannot go from output to input hash^-1(p) -> s. A hash function will ideally map the domain uniformly over the range such that any input that is in the domain will have a pretty even probability of being anywhere in the range vs more likely to be in a certain section.

You use hashing operations for many...

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We keep giving you answers that do not directly answer the question, because that is how we solve this problem. An index of unlimited length is impractical and inefficient, but a unique hash provides a solution that sufficient to the task because of the astronomically low likelihood of a meaningful collision.

Similar to the other offered solutions, my standard approach does not check for duplicates up front -- it is optimistic in that sense: it relies on constraint checking by the database, with the assumption that most inserts are not duplicates, so there's no point in wasting time trying to determine if they are.

Working, tested example (5.7.16, backwards compatible to 5.6; previous versions do not have a built-in TO_BASE64() function):

CREATE TABLE web_page ( id BIGINT UNSIGNED NOT NULL AUTO_INCREMENT, url LONGTEXT NOT NULL, url_hash CHAR(24) COLLATE ascii_bin, PRIMARY KEY(id), UNIQUE KEY(url_hash), KEY(url(16)) )ENGINE=InnoDB DEFAULT CHARSET=utf8...
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In part one of this series, I covered the basic concepts of data duplication. Before getting to the next installment, I wanted to take a second and apologize to the readers for the long delay between posts. It's a long story, but the good news is I am back and ready to go.

Here in part two, I dig a bit deeper into the guts of how deduplication works and why it's important in ensuring that our personal and business data is continually and efficiently protected. This is a concern for almost every person who backs up their data to the cloud or share's information with friends and families over the network. In the case of some businesses, cloud service providers might be the preferred method to back up files in the event of a disaster or hardware failure. This is why many service providers and enterprises rely upon the method of deduplication to keep storage costs in check. Expanding upon my last blog on the business benefits of deduplication, let's dive into the details of...

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This library enables the efficient identification of near-duplicate documents using simhash using a C++ extension.

simhash differs from most hashes in that its goal is to have two similar documents produce similar hashes, where most hashes have the goal of producing very different hashes even in the face of small changes to the input.

The input to simhash is a list of hashes representative of a document. The output is an unsigned 64-bit integer. The input list of hashes can be produced in several ways, but one common mechanism is to:

tokenize the document consider overlapping shingles of these tokens (simhash.shingle) hash these overlapping shingles input these hashes into simhash.compute

This has the effect of considering phrases in a document, rather than just a bag of the words in it.

Once we've produced a simhash, we would like to compare it to other documents. For two documents to be considered near-duplicates, they must have few...

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A) Study the page cache miss rate by using iostat(1) to monitor disk reads, and assume these are cache misses, and not, for example, O_DIRECT. The miss rate is usually a more important metric than the ratio anyway, since misses are proportional to application pain. Also use free(1) to see the cache sizes.

B) Drop the page cache (echo 1 > /proc/sys/vm/drop_caches), and measure how much performance gets worse! I love the use of a negative experiment, but this is of course a painful way to shed some light on cache usage.

C) Use sar(1) and study minor and major faults. I don't think this works (eg, regular I/O).

D) Use the cache-hit-rate.stp SystemTap script, which is number two in an Internet search for Linux page cache hit ratio. It instruments cache access high in the stack, in the VFS interface, so that reads to any file system or storage device can be seen. Cache misses are measured via their disk I/O. This also misses some workload types (some are mentioned...

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Is there a way I can have a hash value as input when searching for files and a complete list of files and their locations as output?

This could be helpful when trying to pin point file duplicates. I often times find myself in situations where I have a bunch of files that I know I already have stored in some location but I don't know where. They are essentially duplicates.

For instance, I could have a bunch of files on a portable hard drive, and also hard copies of those files on the internal hard drive of a desktop computer... but I'm not sure of the location! Now if the files are not renamed, I could do a file name search to try to locate the hard copy on the desktop. I could then compare them side by side and in case they are the same I could delete the copy I have on the portable hard drive. But if the files have been renamed on either one of the hard drives this would probably not work (depending on how much the new names differ from the original).

If a...

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Events is a collection of items, similar to blogs, and any other collection of items one creates (real estate listings, list of dog breeds, etc. etc.) using the SS collection type. So, the answer is no, you can't.

What I believe we are asking for here, to put it in Squarespace-Developer terms, is a way to duplicate an item within a collection, aka a 'duplicate a collection item'.

As such, I would recommend anyone interested in this feature not only up-vote this thread, but also visit and up-vote the following thread as well. That is because events is really just another form of a collection of items, similar to blog posts. My thought here is that if SS added the ability to duplicate an item within a collection (blog posts, for example), then all items within any collections would be "duplicatable" as well, which would include events. So I suggest you also up-vote the following topic as it has many more votes at the moment, and may increase the chances that SS will...

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March 9, 2016

This article was contributed by Neil Brown

"In-band deduplication" is the process of detecting and unifying duplicate data blocks as files are being written, rather than at some later time. Btrfs support for this feature has been under development since at least early 2013. Quite recently it reached the point where developer Qu Wenruo thought it was sufficiently ready to send Btrfs maintainer Chris Mason a pull request (as yet unanswered) hoping that it might be added to the kernel during the 4.6 merge window. While this is far from a magic bullet that will suddenly remove all the waste in your filesystem that is caused by duplicate data, there are use cases where it could bring real benefits.

Offline and in-band

It has been possible to unify duplicated blocks in Btrfs since Linux 3.12 when the BTRFS_IOC_FILE_EXTENT_SAME ioctl() command was added (it has since been renamed FIDEDUPERANGE when the basic ioctl() handling was moved to...

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Rob MacKay wrote:So I've been debugging this on and off for a few days and it seems that for some reason, no matter how many objects are currently in my TreeSet, when a new object is being added, it doesn't appear that compareTo is being called against every other object in the TreeSet. It's my understanding that when adding a new object in the TreeSet, it should be compared against every other object to ensure that it is not already present.

No, not at all. Since the data in the

TreeSet

is sorted already, every time you add a new object, it only calls the compareTo() enough times to find the appropriate spot in the

TreeSet

to add the new object, and to check if there is already an equal object

at that particular spot

. No need to compare against all the other object that are nowhere nearby.

As an example, if your TreeSet contains Strings for each letter of the alphabet, like "A", "B", "C", ... "Z", and you want to add the String "Rob", then...

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This page seeks to teach you how to clone items and blocks without building a separate farm for them, in vanilla Survival mode. It can be treated as an "everything farm," since it can "farm" things that can't normally be farmed, like diamonds and dragon eggs.

Note that these techniques are considered "cheating" by many people and Mojang tries to remove the ability to duplicate in Survival at almost every update. On some servers, these techniques are ban-able offenses.

The best way to duplicate a block is to turn it into an item first, then duplicate the item. But, there are other ways to duplicate the blocks themselves, though they only work for certain blocks. Shulker boxes make item duplication much easier because they allow for up to 1,729 items to be cloned at once (including the shulker box).

Duplicating items and blocks can be very useful. For example, let's say you wanted to obtain enormous amounts of glass or sand. Going into a desert and mining it...

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In a medium sauce pan combine water, salt, brown sugar, lemon zest, bay leaves and pepper corns. Bring to a boil over medium high heat and cook until salt and sugar dissolve. Remove from heat and let steep 15 minutes. Pour mixture over ice water to cool. Once cool to the touch pour mixture over trout, cover, and refrigerate for 2 hours.

Remove fish from brine discarding brine. Rinse filets and pat dry. Lay them out on a cooling rack and refrigerate overnight to form the pellicle. This allows the smoke to adhere to the filets a little better.

When ready to cook, start the Traeger grill on Smoke with the lid open until the fire is established (4 to 5 minutes). Leave the temperature setting on smoke and allow to preheat 10-15 minutes.

Place the filets directly on the grill grate and smoke for 1 1/2-2 hours or until fish begins to flake.

Increase the temperature to 375 and preheat, lid closed, 10-15 minutes.

Heat oil in a cast iron skillet over medium...

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11.1.2.1 Query Transformation

Each query portion of a statement is called a query block. The input to the query transformer is a parsed query, which is represented by a set of query blocks.

In the following example, the SQL statement consists of two query blocks. The subquery in parentheses is the inner query block. The outer query block, which is the rest of the SQL statement, retrieves names of employees in the departments whose IDs were supplied by the subquery.

SELECT first_name, last_name FROM employees WHERE department_id IN (SELECT department_id FROM departments WHERE location_id = 1800);

The query form determines how query blocks are interrelated. The transformer determines whether it is advantageous to rewrite the original SQL statement into a semantically equivalent SQL statement that can be processed more efficiently.

The query transformer employs several query transformation techniques, including the following:

Any combination of...

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Some schema objects refer to other objects, creating a schema object dependency.

For example, a view contains a query that references tables or views, while a PL/SQL subprogram invokes other subprograms. If the definition of object A references object B, then A is a dependent object on B, and B is a referenced object for A.

Oracle Database provides an automatic mechanism to ensure that a dependent object is always up to date with respect to its referenced objects. When you create a dependent object, the database tracks dependencies between the dependent object and its referenced objects. When a referenced object changes in a way that might affect a dependent object, the database marks the dependent object invalid. For example, if a user drops a table, no view based on the dropped table is usable.

An invalid dependent object must be recompiled against the new definition of a referenced object before the dependent object is usable. Recompilation occurs...

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