Forcibly reinterprets its bit unsigned integer argument as signed, and also expands it to bit type because bit type is unsigned. It's easily illustrated by the following example: normally evaluates to , but SINT evaluates to It takes 3 arguments, check whether the 1st argument is equal to 0.
Note that unlike comparison operators, IF does not use a threshold!
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Therefore, it's safe to use comparison results as its 1st argument, but arithmetic operators might produce unexpected results. For instance, the following two calls will produce different results even though they are logically equivalent:. But its value will be slightly different from zero because of limited floating point calculations precision. Because of that, the comparison with 0.
IN expr,val1,val2, Currently, all the checked values but not the expression itself! Its technically possible to implement arbitrary expressions too, and that might be implemented in the future. Constants are pre-sorted and then binary search is used, so IN even against a big arbitrary list of constants will be very quick. First argument can be JSON attribute since 2. It applies to JSON arrays and returns 1 if condition is true for all elements in array and 0 otherwise.
It works similar to ALL except for it returns 1 if condition is true for any element in array. Returns the arctangent function of two arguments, expressed in radians.
Note that by default both latitudes and longitudes must be in radians and the result will be in meters. You can use arbitrary expression as any of the four coordinates. An optimized path will be selected when one pair of the arguments refers directly to a pair attributes and the other one is constant. Starting with version 2. The complete syntax and a few examples are as follows:.
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Upto version 2. First argument is JSON array and return value is the greatest value in that array. Also works for MVA. It iterates through all elements in array and returns index of first element for which 'cond' is true and -1 if 'cond' is false for every element in array. First argument is JSON array and return value is the least value in that array.
It works with both bit and bit MVA attributes. It returns length of a field in JSON. Return value depends on type of a field. Returns sort key value of the worst found element in the current top-N matches if sort key is float and 0 otherwise. This function works only if expression ranker is specified and the query is not a full scan, otherwise it will return an error.
Calculating ATC slows down query processing considerably, so this option can be useful if you need to see the ranking factors, but do not need ATC. The respective outputs in either key-value pair or JSON format would look as follows below. Note that the examples below are wrapped for readability; actual returned values would be single-line. In the example just above, we used an expression-based ranker with a dummy expression, and sorted the result set by the value computed by our UDF.
In other words, we used the UDF to rank all our results. Assume now, for the sake of an example, that our UDF is extremely expensive to compute and has a throughput of just 10, calls per second. Assume that our query matches 1,, documents. To maintain reasonable performance, we would then want to use a much simpler expression to do most of our ranking, and then apply the expensive UDF to only a few top results, say, top results.
Or, in other words, build top results using a simpler ranking function and then re-rank those with a complex one. We can do that just as well with subselects:. So it will get called 1,, times. But the UDF computation can be postponed until the outer sort.
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So the UDF will only get called times. And then the final top matches by UDF value will be selected and returned to the application. This makes it technically feasible to implement additional re-ranking pass or passes on the master node, if needed. Note that this feature is not currently supported by the Sphinx API.
REMAP condition, expression, cond1, cond2, It allows you to make some exceptions of an expression values depending on condition values.
Condition expression should always result integer, expression can result in integer or float. All other modes require an additional sorting clause, with the syntax depending on specific mode. The segments are calculated according to the current timestamp at the time when the search is performed, so the results would change over time. The segments are as follows:. This mode was added to support searching through blogs, news headlines, etc. When using time segments, recent records would be ranked higher because of segment, but within the same segment, more relevant records would be ranked higher - unlike sorting by just the timestamp attribute, which would not take relevance into account at all.
Both internal attributes that are computed by the engine on the fly and user attributes that were configured for this index are allowed. Internal attribute names must start with magic -symbol; user attribute names can be used as is. In the example above, relevance and id are internal attributes and price is user-specified.
Expression sorting mode lets you sort the matches by an arbitrary arithmetic expression, involving attribute values, internal attributes id and weight , arithmetic operations, and a number of built-in functions. In theory, this could be performed by doing only the full-text search in Sphinx and then using found IDs to group on SQL server side.
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However, in practice doing this with a big result set 10KM matches would typically kill performance. To avoid that, Sphinx offers so-called grouping mode. When grouping, all matches are assigned to different groups based on group-by value. This value is computed from specified attribute using one of the following built-in functions:.
The final search result set then contains one best match per group. Grouping function value and per-group match count are returned along as "virtual" attributes named group and count respectively. In addition to id and weight , group-by sorting clause may also include:. The default mode is to sort by groupby value in descending order, ie. To scale well, Sphinx has distributed searching capabilities. Distributed searching is useful to improve query latency ie. This is essential for applications which need to search through huge amounts data ie.
The key idea is to horizontally partition HP searched data across search nodes and then process it in parallel. This index only contains references to other local and remote indexes - so it could not be directly reindexed, and you should reindex those indexes which it references instead. When searchd receives a query against distributed index, it does the following:. From the application's point of view, there are no differences between searching through a regular index, or a distributed index at all. That is, distributed indexes are fully transparent to the application, and actually there's no way to tell whether the index you queried was distributed or local.
Even though as of 0. Any searchd instance could serve both as a master which aggregates the results and a slave which only does local searching at the same time. This has a number of uses:. It is scheduled to implement better HA support which would allow to specify which agents mirror each other, do health checks, keep track of alive agents, load-balance requests, etc. In version 2.
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Previous versions only supported a custom plain text format. That format is still the default one. However, while it might be more convenient for manual monitoring and review, but hard to replay for benchmarks, it only logs search queries but not the other types of requests, does not always contain the complete search query data, etc. The default text format is also harder and sometimes impossible to replay for benchmarking purposes. The new sphinxql format alleviates that.