Indexing issue with Google to my Adult related products

Hello Webmasters,
SEMrush

I’m experiencing “
Warning
Child sexual abuse imagery is illegal” while trying to check my site’s page index in Google.

It is an online store offering adult related products similar to lovehoney.com,
While checking my site:luluandlolita.com, it shows warning with 0 pages but while checking the same for lovehoney, it is showing number of pages.
can check it for https://luluandlolita.com/
Any one who have experience with the sex toys or adult related products optimization, can help me better in this issue.
Is this issue due to its products which are Adult related? if so, then why not with Lovehoney ?
If not, then what is wrong I have done on my site?
Even in my search console, it is showing 392 pages under Coverage>Submitted and indexed with the status:Valid.
and whecking the same page by typing the exact url in google (as per search console), it is also shows zero result.

kindly help me in this issue.

 

postgresql – Creating a specific index tree structure from keys that have the tree-structure baked in? Indexing H3 Hexes

Preface: I am NOT a DBA, please be gentle on my terminology and explanations.


I have data that already has a structure baked into it and need to create an index in my DB that represents the hierarchical relationships in this data. These are actually indexes from Uber H3 (https://h3geo.org/docs/core-library/h3indexing). Each index is a 64-bit value, and a hierarchical relationship already exists within this index, something I wish to take advantage of.

This data describes individual hexagons in the H3 Hierarchal Geospatial Indexing System. Each hexagon has a unique index that describes itself and all ‘parent’ hexagons up to the base-cells. Since each hexagon is subdivided into 7 more, and each of those into 7 more, this 64-bit index already has a tree-structure baked into it.

The Index:

The H3 index (described here) describes a hierarchical relationship between hexagons starting with the 122 base cells that can then be recursively subdivided into 7 children as visualized here: enter image description here

enter image description here

This data pretty much has a tree structure baked into it. The index (8d299d10145543f for example) describes the base-cell index, and the index of each hexagon down to itself. There are 15 resolutions (I’m only using 13), each resolution is the subdivision of the resolution above.

This exists as bit 20 -> 64, with each resolution having 3-bits with values between 0 -> 6 in the 64-bit index. Bit 13 -> 19 representing the 122 base cells.

Index Example:

enter image description here

  • Index Hex: 8d299d10145543f
  • Index Binary: 0000100011010010100110011101000100000001010001010101010000111111
  • Index Hierarchy: 0010100 -> 110 -> 011 -> 101 -> 000 -> 100 -> 000 -> 001 -> 010 -> 001 -> 010 -> 101 -> 010 -> 000

As you can see that the 64-bit index already has a tree-structure in it. I wish to utilize this in the DB index.

I will use this how?

I have a need to store lots (billions) of cells at a specific resolution (13). The primary key being the 64-bit H3 index. This will be read-heavy, write speed isn’t a concern. I imagine that I’ll have partitions for each of the 122 base cells, though that is a best guess on my part…

Writing

Writing would just be adding a few hundred thousand rows at a time. I’m not to concerned with write speed, for my read-heavy use case I could even do a once-a-day bulk write.

Reading

Reading is where things get bit trickier. I will have all the keys I need beforehand, say 100k of them, and just need to retrieve those 100k rows. This gets somewhat messy (Imagine a 100k value IN() query…). Thankfully H3 provides a way for me to ‘compress’ this mass of individual indexes into their ideal parents (So I get an array of indexes at various resolutions that when unpacked at my specific resolution (13) I get the same 100k indexes back). This would leave me ~10k indexes at varying resolution levels to retrieve.

I would then want to retrieve all rows that have these specific parents (regardless of res), which is already represented by the 3-bit res levels as shown in the examples above. For instance retrieving every row that falls under the 0010100 -> 110 -> 011 -> 101 -> 000 -> 100 node.

My objective is to get this data for these indexes back as fast as possible, ideally several times faster than it would take me to re-generate it from scratch (~600ms to generate ~150k cells and their data)


As for DBMS, it will probably be PostgresSQL. I’m familiar with MySQL & MS SQL Server, but the later has prohibitive licensing costs and the former seems like a poor fit vs postgres. Please formulate answers with that in mind, though if you have opinions on a more appropriate technology, I’m all ears.

I do understand that the memory requirements for this will get quite steep… However, I’m willing to chew hundreds of gigs of RAM to get the read speeds I need. Right now I compute all the data on-demand, and my computational needs are way too high, my aim is to reduce that by storing the computed data in a DB and retrieving it instead.

My Questions in order of importance:

  1. Are my performance expectations reasonable?
  2. How can I create an index that can efficiently retrieve data following the structure that already exists in the H3 index?
  3. How do I target specific tree-levels to retrieve all rows that fall under that node in a query?
  4. How do I handle queries that need to reference tens of thousands of keys?

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SharePoint 2019 Crawl of XML Files Is Not Indexing All Desired Fields

SharePoint 2019 Search – Crawling of XML Files Is Not Getting/Indexing All Desired Fields

In the past, when I was working with Search Server Express 2010, I used an xml iFilter from iFilterShop that allowed fields such as the following “Engine Oil Pressure” to be indexed:

<acronym id="106" showdefinition="y" text="Engine Oil Pressure" name="EOP"/>

The iFilter config file had items such as:

<metadata>
    <default>
    </default>
    <exclude>//*</exclude>
    <include>
       <mapping>
         <xpath>//acronym/@text</xpath>
          <property><name>AcronymText</name></property>
       </mapping>

SharePoint 2019 does not crawl such items by default and the above iFilter does not support SP 2019.

Is there some setting within SharePoint Central Administration that really can be set up to do this, and I am just missing it?

If not, do vendors even exist that have such xml iFilters available for SP 2019? (just wondering if there is such a thing)

I know there are Search Schema and Managed Properties, but I believe these are for managing items that have already been crawled.

Relevanssi Search Plugin not indexing all post_title attributes

After digging deeper into how the search works I could find the answer:
In order not to index too many words and maintain performance for searches relevanssi includes so called “stop-words”.
If content marked for indexing contains a stop-word it will be prevented from being indexed.

The solution was to disable stop-words for post_titles with the following filter relevanssi provides:

add_filter('relevanssi_remove_stopwords_in_titles', false, 10, 1);

Also you can adapt the stopwords (and thus the words/sentences being indexed) in the relevanssi settings.

indexing – SOLR Admin Console is not responding normally

I recently upgraded SOLR from version 8.4.1 to 8.6.3. But, after creating the core, and indexing the same (through Hybris Backoffice), I noticed a peculiar behavior in SOLR Admin Console. When I select the particular Core and try to go to “Query” section, it is automatically redirect to Dashboard. So, I am unable to use SOLR Admin console to check the data or query over the indexed data. It is happening to all other sections that come after selecting the core. In my earlier version, I never faced such issues.

I actually need to upgrade to this particular version of SOLR since hybris does only support this version after SOLR 7.7.x.

Is there anyone who faced this issue? Please help if anyone knew the solution.

algorithms – Indexing a huge dataset (that does not fit into central memory)

The problem.

Let us consider a huge file with billions of lines, each containing a string. There are $n$ different strings and $m$ lines in the file, with $m$ much greater than $n$, although both are huge. In particular, neither the entire file nor the list of all distinct strings fit into central memory. The global file may be parsed and/or sorted (in external memory) a few times, though.

The goal is to build the file in which line $i$, for $i=1..m$, is integer $k$ if the string on line $i$ in the original file is the $k+1$-th distinct string appearing in the original file.

Example.

For instance, if the original file is:

bbb
cccc
cccc
bbb
aaa
ee
aaa
fff
dddd
cccc
bbb
fff

then $n=6$, $m=12$, and we should obtain:

0
1
1
0
2
3
2
5
4
1
0
5

My approach.

I take benefit of standard unix command-line tools, as follows.

I first build a numbered string occurence file:

zcat input.gz | awk '{print NR-1,$0;}' | gzip -c > occurrences.gz

I use it to build an index file for strings that respects occurence rank, and starts with character ‘-‘, lower than any occurence number:

zcat occurrences.gz |
  sort -T. -S1g -k2,2 |
  awk 'BEGIN{old="none";}{if ($2!=old) print $0; old=$2;}' |
  sort -T. -S1g -nk1,1 | awk '{print "-",$2,NR-1;}' |
  gzip -c > index.gz

I merge the two files and sort the result to ensure that the line with a string index will be just before all other lines containing this string; I then replace each string, sort according to occurence rank, and I am done:

zcat occurrences.gz index.gz |
  sort -T. -S1g -k2,2 -k1,1 |
  awk '{if ($1=="-") i=$3; else print $1,i;}' |
  sort -T. -S1g -nk1,1 |
  cut -d" " -f2 |
  gzip -c > result.gz

Technical detail: to avoid encoding issues, first set environment variable LC_ALL to C.

Question.

Is it possible to do significantly better than my approach, or to achieve similar performances with a significantly different approach, in practice?

Notice that I am not interested in minor, technical improvements, like using mawk, sed, etc, instead of awk. I am not interested in a purely theoretical solution either, that would be better asymptotically but would turn out to be so complex than no implementation is reasonably feasible.

Instead, my concern is the relevance and overall effectiveness of my approach, or variants.

For instance, would any external memory data structure be faster, while using as little memory, with available implementation?

sorting – indexing huge dataset

The problem.

Let us consider a huge file with billions of lines, each containing a string. There are $n$ different strings and $m$ lines in the file, with $m$ much greater than $n$, although both are huge. In particular, neither the entire file nor the list of all distinct strings fit into central memory. The global file may be parsed and/or sorted (in external memory) a few times, though.

The goal is to build the file in which line $i$, for $i=1..m$, is integer $k$ if the string on line $i$ in the original file is the $k+1$-th distinct string appearing in the original file.

Example.

For instance, if the original file is:

bbb
cccc
cccc
bbb
aaa
ee
aaa
fff
dddd
cccc
bbb
fff

then $n=6$, $m=12$, and we should obtain:

0
1
1
0
2
3
2
5
4
1
0
5

My approach.

I take benefit of standard unix command-line tools, as follows.

I first build a numbered string occurence file:

zcat input.gz | awk '{print NR-1,$0;}' | gzip -c > occurrences.gz

I use it to build an index file for strings that respects occurence rank, and starts with character ‘-‘, lower than any occurence number:

zcat occurrences.gz |
  sort -T. -S1g -k2,2 |
  awk 'BEGIN{old="none";}{if ($2!=old) print $0; old=$2;}' |
  sort -T. -S1g -nk1,1 | awk '{print "-",$2,NR-1;}' |
  gzip -c > index.gz

I merge the two files and sort the result to ensure that the line with a string index will be just before all other lines containing this string; I then replace each string, sort according to occurence rank, and I am done:

zcat occurrences.gz index.gz |
  sort -T. -S1g -k2,2 -k1,1 |
  awk '{if ($1=="-") i=$3; else print $1,i;}' |
  sort -T. -S1g -nk1,1 |
  cut -d" " -f2 |
  gzip -c > result.gz

Technical detail: to avoid encoding issues, first set environment variable LC_ALL to C.

Question.

Is it possible to do significantly better than my approach, or to achieve similar performances with a significantly different approach, in practice?

Notice that I am not interested in minor, technical improvements, like using mawk, sed, etc, instead of awk. I am not interested in a purely theoretical solution either, that would be better asymptotically but would turn out to be so complex than no implementation is reasonably feasible.

Instead, my concern is the relevance and overall effectiveness of my approach, or variants.

For instance, would any external memory data structure be faster, while using as little memory, with available implementation?