One of the challenges of big data is that it is, well, big.
Computers are optimized for math on 64 bits or less. Any bigger,
and extra steps have to be taken to work with the data which is
very expensive. This is why a BIGINT is 64 bits. In MySQL
DECIMAL can store more than 64 bits of data using fixed
precision. Large numbers can use FLOAT or DECIMAL but those
data types are lossy.

DECIMAL is an expensive encoding. Fixed precision math is
expensive and you eventually run out of precision at which point
you can't store any more data, right?

What happens when you want to store a counter that is bigger than
the maximum DECIMAL? FLOAT is lossy. What if you need
an /exact/ count of a very big number without using very much
space?

I've developed an encoding method that allows you to store very
large counters in a very small amount of space. It takes
advantage of the fact that counters …

**1**to

**2**

**bit length encoding**(reset)

One of the challenges of big data is that it is, well, big.
Computers are optimized for math on 64 bits or less. Any bigger,
and extra steps have to be taken to work with the data which is
very expensive. This is why a BIGINT is 64 bits. In MySQL
DECIMAL can store more than 64 bits of data using fixed
precision. Large numbers can use FLOAT or DECIMAL but those
data types are lossy.

DECIMAL is an expensive encoding. Fixed precision math is
expensive and you eventually run out of precision at which point
you can't store any more data, right?

What happens when you want to store a counter that is bigger than
the maximum DECIMAL? FLOAT is lossy. What if you need
an /exact/ count of a very big number without using very much
space?

I've developed an encoding method that allows you to store very
large counters in a very small amount of space. It takes
advantage of the fact that counters …

**1**to

**2**