Re: Fast.times 720p Mkv Index Of
Padre Harmon
Given I have a HUGE array, and a value from it. I want to get index of the value in array. Is there any other way, rather then call Array#index to get it? The problem comes from the need of keeping really huge array and calling Array#index enormous amount of times.
You can use binary search (if your array is ordered and the values you store in the array are comparable in some way). For that to work you need to be able to tell the binary search whether it should be looking "to the left" or "to the right" of the current element. But I believe there is nothing wrong with storing the index at insertion time and then using it if you are getting the element from the same array.
Ran the explain plan and it seems identical between the new and updated query. Only difference is the the MERGE JOIN operation is 16,269 bytes in the old query and 1,218 bytes in the new query. Actually cardinality is higher in the old query as well. And I actually don't see an "INDEX" operation on the old or new query in the explain plan, just for the index on the destinationnumber field.
Anyway, there's nothing you can do to the index to change the way databases work. You could make things faster with partitioning, but I would guess if your organisation had bought the Partitioning option you'd be using it already.
A full tablescan would be faster. This happens if a substantial fraction of rows has to be retrieved. The concrete numbers depend on various factors, but as a rule of thumb in common situations using an index is slower if you retrieve more than 10-20% of your rows.
Another note on the "skewed data issue": There are cases the optimizer detects skewed data in column A if you have an index on cloumn A but not if you only have a combined index on columns A and B, because the combination might make the distribution more even. This is one of the few cases where an index on A,B does not make an index on A redundant.
In my test using PostgreSQL 9.6.1, a table with three double precision columns and 10M records with random values, creating an index on the same data, but preordered, shaved off 70% of index creation time:
There are two kinds of indexes that can be used to speed up full text searches. Note that indexes are not mandatory for full text searching, but in cases where a column is searched on a regular basis, an index is usually desirable.
A GiST index is lossy, meaning that the index may produce false matches, and it is necessary to check the actual table row to eliminate such false matches. (PostgreSQL does this automatically when needed.) GiST indexes are lossy because each document is represented in the index by a fixed-length signature. The signature is generated by hashing each word into a single bit in an n-bit string, with all these bits OR-ed together to produce an n-bit document signature. When two words hash to the same bit position there will be a false match. If all words in the query have matches (real or false) then the table row must be retrieved to see if the match is correct.
Lossiness causes performance degradation due to unnecessary fetches of table records that turn out to be false matches. Since random access to table records is slow, this limits the usefulness of GiST indexes. The likelihood of false matches depends on several factors, in particular the number of unique words, so using dictionaries to reduce this number is recommended.
GIN indexes are not lossy for standard queries, but their performance depends logarithmically on the number of unique words. (However, GIN indexes store only the words (lexemes) of tsvector values, and not their weight labels. Thus a table row recheck is needed when using a query that involves weights.)
As a rule of thumb, GIN indexes are best for static data because lookups are faster. For dynamic data, GiST indexes are faster to update. Specifically, GiST indexes are very good for dynamic data and fast if the number of unique words (lexemes) is under 100,000, while GIN indexes will handle 100,000+ lexemes better but are slower to update.
Partitioning of big collections and the proper use of GiST and GIN indexes allows the implementation of very fast searches with online update. Partitioning can be done at the database level using table inheritance, or by distributing documents over servers and collecting search results using the dblink module. The latter is possible because ranking functions use only local information.
A new chromatographic hydrophobicity index (CHI) is described which can be used as part of a protocol for high-throughput (50-100 compounds/day) physicochemical property profiling for rational drug design. The index is derived from retention times (t(R)) observed in a fast gradient reversed-phase HPLC method. The isocratic retention factors (log k') were measured for a series of 76 structurally unrelated compounds by using various concentrations of acetonitrile in the mobile phase. By plotting the log k' as a function of the acetonitrile concentration, the slope (S) and the intercept (log k'(w)) values were calculated. The previously validated index of hydrophobicity φ(0) was calculated as -log k'(w)/S. A good linear correlation was obtained between the gradient retention time values, t(R) and the isocratically determined φ(0) values for the 76 compounds. The constants of this linear correlation can be used to calculate CHI. For most compounds, CHI is between 0 and 100 and in this range it approximates to the percentage (by volume) of acetonitrile required to achieve an equal distribution of compound between the mobile and the stationary phases. CHI values can be measured using acidic, neutral, or slightly basic eluents. Values corresponding to the neutral form of molecules could be measured for 52 of the compounds and showed good correlation (r = 0.851) to the calculated octanol/water partition coefficient (c log P) values.
While the pgvector extension with IVFFlat indexing has been a popular choice, our new pg_embedding extension uses Hierarchical Navigable Small Worlds (HNSW) index to unlock new levels of efficiency in high-dimensional similarity search.
For those curious about the inner workings and the differences between IVFFlat and HNSW for Postgres applications, we carried out benchmark tests on Neon Postgres to compare the performance of the two indexes. Keep on reading to find out more.
The benchmark tests compare the performance of pg_embedding with HNSW and pgvector with IVFFlat indexing using the GIST-960 Euclidean dataset, which provides a train set of 1 million vectors of 960 dimensions, and a test set of 1000. Each search returned k=100 vectors.
pgvector allows for vector similarity search directly within the database. One of its indexing techniques is called IVFFlat. The IVFFlat index partitions the dataset into multiple clusters and maintains an inverted list for each cluster. During search, only a selected number of clusters are examined, which greatly speeds up the search process compared to a flat index.
HNSW is a graph-based approach to indexing high-dimensional data. It constructs a hierarchy of graphs, where each layer is a subset of the previous one, which results in a time complexity of O(log(rows)). During search, it navigates through these graphs to quickly find the nearest neighbors.
With the introduction of the pg_embedding extension for Postgres, you now have a powerful new tool at your disposal for handling high-dimensional vector similarity searches efficiently within your database. The graph-based nature of the HNSW algorithm offers several advantages over the IVFFlat index in terms of search speed, accuracy, and ease of setup.
The inverted index is essentially what powers all the GraphQL where filters, where vectors or semantics are needed to find results. With inverted indexes, contents or data object properties such as words and numbers are mapped to its location in the database. This is the opposite of the more traditional forward index, which maps from documents to its content.
Inverted indices are used often in document retrieval systems and search engines, because it allows fast full-text search and fast key-based search instead of brute-force. This fast data retrieval comes with the only cost of slight increase of processing time when a new data object is added, since the data object will indexed and stored in an inverted way, rather than only storing the index of the data object. In the database (Weaviate), there is a big lookup table which contains all the inverted indices. If you want to retrieve objects with a specific property or content, then the database starts looking for only one row with this property which points to the relevant data objects (the row contains pointers to the data object IDs). This makes data object retrieval with these kind of queries very fast. Even if there are more than a billion entries, if you only care about the entries that contain the specific words or properties you're looking for, only one row will be read with the document pointers.
The inverted index currently does not do any weighing (e.g. tf-idf) for sorting, since the vector index is used for these features like sorting. The inverted index is thus, at the moment, rather a binary operation: including or excluding data objects from the query result list, which results in an 'allow list'.
Everything that has a vector, thus every data object in Weaviate, is also indexed in the vector index. Although Weaviate currently supports HNSW vector indexes, it is built to be configurable, with more vector index types on the way.
HNSW is the first vector index type supported by Weaviate. Typically for HNSW is that this index type is super fast at query time, but more costly when it comes to building (adding data with vectors). This means that the process of adding data objects might take longer than you expect or to what you are used to (with other database systems for example). Other database systems, like Elasticsearch, do not make use of vector indexing, but only rely on inverted index. By adding vectorization of data with HNSW, semantic and context-based search is enables, with very high performance on query time.
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