Might be a niche in there somewhere to be served. Actually I am observing this news Some people here still thinks that row oriented databases can compete in aggregation speed, so if I do a MVP it will require a lot of teaching. One question. A BT index is just the sorted values of a column with pointers to the row id of the actually rows. Note that BT index are often compressed as well since the sorted values have many similarity to its neighbors.
The difference is in the operations. With a b tree the software operates on 1 row at a time, but when represented as a series of bit, then in a 32 bit CPU you can process 32 rows in 1 instruction. Which leads to bitmap index over GPU Also article describes a word-aligned bitmaps data structure, which uses RLE, so it will skip areas of all-zeros or all-ones.
Yes, the only problem I found when writing my own bitmap index is they become cumbersome when asking the question: what value has this column for this row. RLE does not help there. But they are extremely fast when asking the question: what rows has this value on this column. And that is the whole concept, the ability to flip the question. Thanks, FastBit looks quite interesting, but the site is so So much great research is lurking behind "unassuming" web presences.
It is also worth noting that some of the technology underpinning FastBit is actually patented the Word Aligned Hybrid method of run length encoding. Its a pretty sorry state of affairs when core, relatively simple data structures are patentable.
Imagine if someone had patented the linked-list or binary search tree! It is frustrating The earlier and inferior byte aligned bitmap compression is patent free, as I recall. This is so obvious, that I'm sure there is a prior art.
I used similar RLE encoding for image compression tens of years ago. That, er, seems obvious, and I would have thought has prior art In the US prios art does not count anymore. There are papers since the 60s about bitmap indices, but now is first to file not first to invent Politics screw us up again. Legally, prior art still invalidates a patent.
But that only matters if you can afford to litigate. Link to proof this? I am very curious Column-oriented databases are faster because computers are built for arrays and not rows.
It is a lot easier to pass through a simple column in memory or read a simple column from the disk. The pass through the memory benefits from all the caching functionality on the chip, and the pass through the disk benefits from the arm not having to bounce around.
The primary benefit you can get by storing data in a column-oriented database is that some of your queries could become really fast. Imagine, for example, that you wanted to know the average age of all of your users. So when querying, columnar storage lets you skip over all the non-relevant data very quickly. Hence, aggregation queries queries where you only need to lookup subsets of your total data could become really fast compared to row-oriented databases.
Further, since the data type for each column is similar, you get better compression when running compression algorithms on each column which would make queries even faster. And this is accentuated as your dataset becomes larger and larger. Aggregation is great, but my app needs to show data for each individual user?? There are many cases where you actually do need multiple fields from each row. And columnar databases are generally not great for these types of queries.
The more fields you need to read per record, the less benefits you get from storing in a column-oriented fashion.
In fact, if your queries are for looking up user-specific values only, row-oriented databases usually perform those queries much faster. Notify me of new posts via email. Create a free website or blog at WordPress. Menu About Disclaimer. Organization of Data The fundamental and most obvious difference between column stores and row stores is the way they organize and store table data. Vectorized Query Execution The columnar storage format allows other efficient techniques to be built on top of it to deliver high performance for analytical or data warehouse type of queries.
CPU Cache Friendly The columnar storage format allows better utilization of CPU cache since cache lines are full of related values same column that are needed by query executor to run some operations evaluation, computation etc. Late Materialization Any query eventually needs to send back the result-set tuples to the end user.
What did we do above? There is typically one decision that needs to be made — when do we form a tuple? In other words, when do we materialize values from different columns into a tuple? The fact that it is needed by the query plan for something will dictate the addition of column value to the tuple. We end up forming tuples that eventually get discarded simply because operator evaluation failed on one of the columns. Late Materialization Take advantage of the columnar format and process the individual column first.
For example, in a tight for-loop AGE column was scanned first to do predicate evaluation on all the column values. How did Late Materialization help us? We formed only relevant tuples and that too much later during execution. No CPU was wasted on forming tuples upfront without knowing if a particular tuple might eventually be a part of result-set or not.
This is more efficient utilization of CPU-memory bandwidth. Leveraged the storage format columnar and applied vectorized query processing during predicate evaluation on a particular column. If the column values are going to be materialized into tuples sooner in the query plan, then we lose the opportunity to use vectorized instructions on a column simply because after materialization the data is no longer columnar and we need to work with tuples. Because materialization is done later in the plan, we quickly loop through the values of a column and applied necessary filters.
This is far more efficient than a tuple based iterator that we need to work with in the case of early materialization since for each tuple, we feed the right column value AGE into the operator WHERE and decide if the tuple is needed or not. There are some advantages of using such light-weight compression methods: Compliment vectorized query processing by potentially packing the data uncompressed column values into fixed-width values in an array compressed column values.
SIMD instructions can then work on this well packed data and process the compressed column values. The encoding schemes mostly allow us to work on compressed column values and process them before decompressing everything upfront. This way we can decompress only the needed values — values that passed predicate filters.
Obviously raw column data might already be fixed width — for example AGE column might be internally represented using an array of 4 byte integers. So without doubt, SIMD techniques will be effective on raw column data as well in this case. However, if we compress the AGE column using bit-packing 1 byte is enough to represent any value in AGE column , the processing actually becomes much faster because the number of column values that can be loaded and parallelly processed into SIMD register typically bits are far more if the column is compressed.
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