What is HyperLogLog used for?
A HyperLogLog is a probabilistic data structure used to count unique values — or as it’s referred to in mathematics: calculating the cardinality of a set. These values can be anything: for example, IP addresses for the visitors of a website, search terms, or email addresses.
What is HyperLogLog in Redis?
Redis HyperLogLog is an algorithm that uses randomization in order to provide an approximation of the number of unique elements in a set using just a constant, and small amount of memory. There is no limit to the number of items you can count, unless you approach 264 items.
How HLL works?
HLL works by providing an approximate count of distinct elements using a function called APPROX_DISTINCT . With HLL, we can perform the same calculation in 12 hours with less than 1 MB of memory.
What is HyperLogLog ++ HLL and why is it used in BigQuery?
HyperLogLog (HLL) is an algorithm that estimates how many unique elements the dataset contains. Google BigQuery has leveraged this algorithm to approximately count unique elements for very large dataset with 1 billion rows and above.
When should I use HyperLogLog?
One of the most important things to take away is that HyperLogLog is most effective when you are creating a structure for getting the answers to questions the business will need before you begin collecting the data.
What is HLL sketch?
HLL sketch is a construct that encapsulates the information about the distinct values in the data set. You can use HLL sketches to achieve significant performance benefits for queries that compute approximate cardinality over large data sets, with an average relative error between 0.01–0.6%.
What are Redis streams?
Redis Streams is a new Redis data structure for managing data channels between producers and consumers. These include content caching, session stores, real-time analytics, message brokering, and data streaming. Last year I wrote about how to use Redis Pub/Sub, Lists, and Sorted Sets for real-time stream processing.
Is BigQuery columnar database?
BigQuery leverages the columnar storage format and compression algorithm to store data in Colossus, optimized for reading large amounts of structured data.
How are BigQuery tables ultimately stored?
BigQuery stores data in a columnar format known as Capacitor. As you may expect, each field of BigQuery table i.e. column is stored in a separate Capacitor file which enables BigQuery to achieve very high compression ratio and scan throughput. Once all column data is encoded, it’s written back to Colossus.
What is probabilistic data structure?
Probabilistic data structures are a group of data structures that are extremely useful for big data and streaming applications. Generally speaking, these data structures use hash functions to randomize and compactly represent a set of items.
How accurate is HLL?
As we discussed above, HLL is not 100% accurate. 99% of the time its margin of error is within 1%, with the remaining 1% of the time resulting in even larger margins of error. If the error does happen to be extremely large, it stands to reason that it would lead to extreme problems.
How does HyperLogLog work in a data set?
And that, my friends, is how HyperLogLog fundamentally works: it allows us to estimate uniques within a large dataset by recording the longest sequence of zeroes within that set.
What kind of algorithm does Redis HyperLogLog use?
Redis HyperLogLog is an algorithm that uses randomization in order to provide an approximation of the number of unique elements in a set using just a constant, and small amount of memory. HyperLogLog provides a very good approximation of the cardinality of a set even using a very small amount of memory around 12 kbytes per key with a standard
How is the HyperLogLog algorithm used in Presto?
To speed up these queries, we implemented an algorithm called HyperLogLog (HLL) in Presto, a distributed SQL query engine. HLL works by providing an approximate count of distinct elements using a function called APPROX_DISTINCT. With HLL, we can perform the same calculation in 12 hours with less than 1 MB of memory.
What is the relative accuracy of the HyperLogLog algorithm?
The HyperLogLog algorithm can estimate cardinalities well beyond 10^9 with a relative accuracy (standard error) of 2% while only using 1.5kb of memory. Since this is the usual me oversimplifying things that I find hard to understand, let’s have a look at some more details of HLL.