QueryParse | sql解析和执行，能够执行hive spark flink 以及对应对TensorFlow

Conversations on Slack revealed that it is not possible to sort by n:m values with Extbase. This is only possible with 1:1 dependencies.

We solved it a followed. After getting wanted coursed we sorted them as array. If you want to paginate it then you can use ArrayPaginator from the new PaginatorAPI.

The problem was in incorrect dependencies. Since com.google.guava:failureaccess:1.0, was added as a dependency of Guava in Guava 27.0 and it missed in solr distribution

The solution is to add com.google.guava:failureaccess jar into server/solr-webapp/webapp/WEB-INF/lib/

It seems that this issue has been solved, this lib was added in dependencies in commit, and i believe it will be included in new release https://github.com/apache/solr/commit/be681bd9e0d24085c78c63fe11914faa41f4b813

This may cross the border into "not an answer" - but it is too long for a comment (or a few comments):

Why is this?

That was a design decision, it would seem. It's mentioned in the documentation here.

"The value is between 0 and 2"

There is an old article here which gives an explanation:

"Larger differences are far more expensive to compute efficiently and are not processed by Lucene.".

I don't know how official that is, however.

More officially, from the JavaDoc for the FuzzyQuery class, it states:

"At most, this query will match terms up to 2 edits. Higher distances (especially with transpositions enabled), are generally not useful and will match a significant amount of the term dictionary."

How can I correctly get the fuzzy edit distance I want into the query parser?

You cannot, unless you customize the source code.

The best (least worst?) alternative, I think, is probably the one mentioned in the above referenced FuzzyQuery Javadoc:

"If you really want this, consider using an n-gram indexing technique (such as the SpellChecker in the suggest module) instead."

In this case, one price to be paid will be a potentially much larger index - and even then, n-grams are not really equivalent to edit distances. I don't know if this would meet your needs.

You are mixing two different geocoding requests in one. structured and unstructured. query should only be used on it's own, when you add CountryRegion to the request, it might be interpreted as a structured request and the query parameter ignored.

Also try setting the culture parameter of the URL to zh-Hans or zh-Hant so that the geocoder knows your request is in Chinese and to call into the Chinese data provider for detailed Chinese map data.

Also, be sure to encode your query so that special characters don't cause issues in the request. This is a best practice.

Here is a modified version of your request.

http://dev.virtualearth.net/REST/v1/Locations?query=%E8%97%95%E8%88%AB%E8%B7%AF276%E5%8F%B7&incl=queryParse&culture=zh-Hans&key=

Unfortunately you can't use an old Codec to access index files from Lucene 2.2. This is because codecs were introduced in Lucene 4.0. Prior to that the code for reading and writing files of the index was not grouped together into a codec but rather was just inherently part of the overall Lucene Library.

So in version of Lucene prior to 4.0 there is no codec, just file reading and writing code baked into the library. It would be very difficult to track down all that code and to create a codec that could be plugged into a modern version of Lucene. It's not an impossible task, but it require an Expert Lucene developer and a large amount of effort (ie an extremely expensive endeavor).

In light of all that, the answer to this SO question may be of some use: How to upgrade lucene files from 2.2 to 4.3.1

Your best bet would be to use an old 3.x copy of java lucene or the Lucene.net ver 3.0.3 to open the index, then add and commit one doc (which will create a 2nd segment) and do a Optimize which will cause the two segments to be merged into one new segment. The new segment will be a version 3 segment. Then you can use Lucene.Net 4.8 Beta or a Java Lucene 4.X to do the same thing (but Commit was renamed ForceMerge starting in ver 4) again to convert the index to a 4.x index.

Then you can use the current java version of Lucene 8.x to do this once more to move the index all the way up to 8 since the current version of Java Lucene has codecs reaching all the way back to 5.0 see: https://github.com/apache/lucene-solr/tree/master/lucene/core/src/java/org/apache/lucene/codecs

However if you do receive the error again that you reported:

This version of Lucene only supports indexes created with release 6.0 and later.

then you will have to play this game one more cycle with a version 6.x Java Lucene to get from a 5.x index to a 6.x index. :-)

The error message (in Spanish) says "The system cannot find the specified file." Did you check that java is really installed in the specified path?

Here are two related resources:

• How to install SonarQube with Open JDK 11
• https://community.sonarsource.com/t/java-11-not-detected-although-it-is-installed/14160

As @andrewjames mentions, you don't need to use multiple analyzers in your example because only the TextField gets analyzed, the StringFields do not. If you had a situation where you did need to use different analyzers for different fields, Lucene can accommodate that. To do so you use a PerFieldAnalyzerWrapper which basically let's you specify a default Analyzer and then as many field specific analyzers as you like (passed to PerFieldAnalyzerWrapper as a dictionary). Then when analyzing the doc it will use the field specific analyzer if one was specified and if not, it will use the default analyzer you specified for the PerFieldAnalyzerWrapper.

Whether using a single analyzer or using multiple via PerFieldAnalyzerWrapper, you only need one QueryParser and you will pass that parser either the one analyzer or the PerFieldAnalyzerWrapper which is an analyzer that wraps several analyzers.

The fact that some of your fields are stored and some are not stored has no impact on searching them. The only thing that matters for the search is that the field is indexed, and both StringFields and TextFields are always indexed.

You mention the following:

And I'm using the KeywordAnalyzer to search over the filename Field, which, to reiterate, is stored, so not analyzed.

Whether a field is stored or not has nothing to do with whether it's analyzed. For the filename field your code is using a StringField with Field.Store.YES. Because it's a StringField it will be indexed BUT not analyzed, and because you specified to store the field it will be stored. So since the field is NOT analyzed, it won't be using the KeywordAnalyzer or any other analyzer :-)

Is there a way to search over tokenized and untokenized Fields concurrently?

The real issue here isn't about searching tokenized and untokenized fields concurrently, it's really just about search multiple fields concurrently. The fact that one is tokenized and one is not is of no consequence for lucene. To search multiple fields at once you can use a BooleanQuery and with this query object you can add multiple queries to it, one for each field, and specify an AND ie Must or an OR ie Should relationship between the subqueries.

I hope this helps clear things up for you.

it is a bad idea to use external configuration files in elasticsearch plugin. ES provides a mechanism for extending the elasticsearch configuration. all of your custom config should be put in the elasticsearch.yml along with a custom setting registration in the plugin like so:

First, on the mapping side, you'll just need this: