Thread: Guidelines on best indexing strategy for varying searches on 20+ columns

Hi,
I’m running a search engine for cars. It’s backed by a postgresql 9.3 installation.

Now I’m unsure about the best approach/strategy on doing index optimization for the fronted search.

The problem:

The table containing the cars holds a around 1,5 million rows. People that searches for cars needs different criteria
tosearch by. Some search by brand/model, some by year, some by mileage, some by price and some by special equipment
etc.etc. - and often they combine a whole bunch of criteria together. Of cause some, like brand/mode and price, are
usedmore frequently than others. In total we offer: 9 category criteria like brand/model or body type, plus 5 numeric
criterialike price or mileage, plus 12 boolean criteria like equipment. Lastly people can order the results by
differentcolumns (year, price, mileage and a score we create about the cars). By default we order by our own generated
score.

What I’ve done so far:

I have analyzed the usage of the criteria “lightly”, and created a few indexes (10). Among those, are e.g. indexes on
price,mileage and a combined index on brand/model. Since we are only interested in showing results for cars which is
actuallyfor sale, the indexes are made as partial indexes on a sales state column. 

Questions:

1. How would you go about analyzing and determining what columns should be indexed, and how?
2. What is the best strategy when optimizing indexes for searches happening on 20 + columns, where the use and the
combinationsvaries a lot? (To just index everything, to index some of the columns, to do combined indexes, to only do
singlecolumn indexes etc. etc.) 
3. I expect that it does not make sense to index all columns?
4. I expect it does not make sense to index boolean columns?
5. Is it better to do a combined index on 5 frequently used columns rather than having individual indexes on each of
them?
6. Would it be a goof idea to have all indexes sorted by my default sorting?
7. Do you have so experiences with other approaches that could greatly improve performance (e.g. forcing indexes to
stayin memory etc.)? 

On Wed, Jun 25, 2014 at 3:48 AM, Niels Kristian Schjødt
<nielskristian@autouncle.com> wrote:
> Hi,
> I’m running a search engine for cars. It’s backed by a postgresql 9.3 installation.
>
> Now I’m unsure about the best approach/strategy on doing index optimization for the fronted search.
>
> The problem:
>
> The table containing the cars holds a around 1,5 million rows. People that searches for cars needs different criteria
tosearch by. Some search by brand/model, some by year, some by mileage, some by price and some by special equipment
etc.etc. - and often they combine a whole bunch of criteria together. Of cause some, like brand/mode and price, are
usedmore frequently than others. In total we offer: 9 category criteria like brand/model or body type, plus 5 numeric
criterialike price or mileage, plus 12 boolean criteria like equipment. Lastly people can order the results by
differentcolumns (year, price, mileage and a score we create about the cars). By default we order by our own generated
score.
>
> What I’ve done so far:
>
> I have analyzed the usage of the criteria “lightly”, and created a few indexes (10). Among those, are e.g. indexes on
price,mileage and a combined index on brand/model. Since we are only interested in showing results for cars which is
actuallyfor sale, the indexes are made as partial indexes on a sales state column. 
>
> Questions:
>
> 1. How would you go about analyzing and determining what columns should be indexed, and how?

mainly frequency of access.

> 2. What is the best strategy when optimizing indexes for searches happening on 20 + columns, where the use and the
combinationsvaries a lot? (To just index everything, to index some of the columns, to do combined indexes, to only do
singlecolumn indexes etc. etc.) 

don't make 20 indexes.   consider installing pg_trgm (for optimized
LIKE searching) or hstore (for optmized key value searching) and then
using GIST/GIN for multiple attribute search.  with 9.4 we have
another fancy technique to explore: jsonb searching via GIST/GIN.

> 3. I expect that it does not make sense to index all columns?

well, maybe.  if you only ever search one column at a time, then it
might make sense.  but if you need to search arbitrary criteria and
frequently combine a large number, then no -- particularly if your
dataset is very large and individual criteria are not very selective.

> 4. I expect it does not make sense to index boolean columns?

in general, no.  an important exception is if you are only interested
in true or false and the number of records that have that interesting
value is tiny relative to the size of the table.  in that case, a
partial index can be used for massive optimization.

> 5. Is it better to do a combined index on 5 frequently used columns rather than having individual indexes on each of
them?

Only if you search those 5 columns together a significant portion of the time.

> 6. Would it be a goof idea to have all indexes sorted by my default sorting?

index order rarely matters.  if you always search values backwards and
the table is very large you may want to consider it.  unfortunately
this often doesn't work for composite indexes so sometimes we must
explore the old school technique of reversing the value.

> 7. Do you have so experiences with other approaches that could greatly improve performance (e.g. forcing indexes to
stayin memory etc.)? 

as noted above, fancy indexing is the first place to look.   start
with pg_trgm (for like optmization), hstore, and the new json stuff.
the big limitation you will hit is that that most index strategies, at
least fo the prepackaged stuff will support '=', or partial string
(particularly with pg_trgm like), but not > or <: for range operations
you have to post process the search or try to work the index from
another angle.

merlin

On Wed, Jun 25, 2014 at 1:48 AM, Niels Kristian Schjødt
<nielskristian@autouncle.com> wrote:
> Hi,
> I’m running a search engine for cars. It’s backed by a postgresql 9.3 installation.
>
> Now I’m unsure about the best approach/strategy on doing index optimization for the fronted search.
>
> The problem:
>
> The table containing the cars holds a around 1,5 million rows. People that searches for cars needs different criteria
tosearch by. Some search by brand/model, some by year, some by mileage, some by price and some by special equipment
etc.etc. - and often they combine a whole bunch of criteria together. Of cause some, like brand/mode and price, are
usedmore frequently than others. In total we offer: 9 category criteria like brand/model or body type, plus 5 numeric
criterialike price or mileage, plus 12 boolean criteria like equipment. Lastly people can order the results by
differentcolumns (year, price, mileage and a score we create about the cars). By default we order by our own generated
score.
>
> What I’ve done so far:
>
> I have analyzed the usage of the criteria “lightly”, and created a few indexes (10). Among those, are e.g. indexes on
price,mileage and a combined index on brand/model. Since we are only interested in showing results for cars which is
actuallyfor sale, the indexes are made as partial indexes on a sales state column. 

I'd probably partition the data on whether it is for sale, and then
search only the for-sale partition.

>
> Questions:
>
> 1. How would you go about analyzing and determining what columns should be indexed, and how?

I'd start out with intuition about which columns are likely to be used
most often, and in a selective way.  And followup by logging slow
queries so they can be dissected at leisure.

> 2. What is the best strategy when optimizing indexes for searches happening on 20 + columns, where the use and the
combinationsvaries a lot? (To just index everything, to index some of the columns, to do combined indexes, to only do
singlecolumn indexes etc. etc.) 

There is no magic index.  Based on your description, you are going to
be seq scanning your table a lot.  Focus on making it as small as
possible, but vertical partitioning it so that the not-for-sale
entries are hived off to an historical table, and horizontally
partitioning it so that large columns rarely used in the where clause
are in a separate table (Ideally you would tell postgresql to
aggressively toast those columns, but there is no knob with which to
do that)


> 3. I expect that it does not make sense to index all columns?

You mean individually, or jointly?  Either way, probably not.

> 4. I expect it does not make sense to index boolean columns?

In some cases it can, for example if the data distribution is very
lopsided and the value with the smaller side is frequently specified.

> 5. Is it better to do a combined index on 5 frequently used columns rather than having individual indexes on each of
them?

How often are the columns specified together?  If they are completely
independent it probably makes little sense to index them together.

> 6. Would it be a goof idea to have all indexes sorted by my default sorting?

You don't get to choose.  An btree index is sorted by the columns
specified in the index, according to the operators specified (or
defaulted).  Unless you mean that you want to add the default sort
column to be the lead column in each index, that actually might make
sense.

> 7. Do you have so experiences with other approaches that could greatly improve performance (e.g. forcing indexes to
stayin memory etc.)? 

If your queries are as unstructured as you imply, I'd forget about
indexes for the most part, as you will have a hard time findings ones
that work.  Concentrate on making seq scans as fast as possible.  If
most of your queries end in something like "ORDER by price limit 10"
then concentrate on index scans over price.  You will probably want to
include heuristics in your UI such that if people configure queries to
download half your database, you disallow that.  You will probably
find that 90% of the workload comes from people who are just playing
around with your website and don't actually intend to do business with
you.

Cheers,

Jeff

Thanks for the answers.

> Den 30/06/2014 kl. 20.04 skrev Jeff Janes <jeff.janes@gmail.com>:
>
> On Wed, Jun 25, 2014 at 1:48 AM, Niels Kristian Schjødt
> <nielskristian@autouncle.com> wrote:
>> Hi,
>> I’m running a search engine for cars. It’s backed by a postgresql 9.3 installation.
>>
>> Now I’m unsure about the best approach/strategy on doing index optimization for the fronted search.
>>
>> The problem:
>>
>> The table containing the cars holds a around 1,5 million rows. People that searches for cars needs different
criteriato search by. Some search by brand/model, some by year, some by mileage, some by price and some by special
equipmentetc. etc. - and often they combine a whole bunch of criteria together. Of cause some, like brand/mode and
price,are used more frequently than others. In total we offer: 9 category criteria like brand/model or body type, plus
5numeric criteria like price or mileage, plus 12 boolean criteria like equipment. Lastly people can order the results
bydifferent columns (year, price, mileage and a score we create about the cars). By default we order by our own
generatedscore. 
>>
>> What I’ve done so far:
>>
>> I have analyzed the usage of the criteria “lightly”, and created a few indexes (10). Among those, are e.g. indexes
onprice, mileage and a combined index on brand/model. Since we are only interested in showing results for cars which is
actuallyfor sale, the indexes are made as partial indexes on a sales state column. 
>
> I'd probably partition the data on whether it is for sale, and then
> search only the for-sale partition.

Hmm okay, I already did all the indexes partial based on the for-sales state. If the queries always queries for-sale,
andall indexes are partial based on those, will it then still help performance / make sense to partition the tables? 
>
>>
>> Questions:
>>
>> 1. How would you go about analyzing and determining what columns should be indexed, and how?
>
> I'd start out with intuition about which columns are likely to be used
> most often, and in a selective way.  And followup by logging slow
> queries so they can be dissected at leisure.
>
>> 2. What is the best strategy when optimizing indexes for searches happening on 20 + columns, where the use and the
combinationsvaries a lot? (To just index everything, to index some of the columns, to do combined indexes, to only do
singlecolumn indexes etc. etc.) 
>
> There is no magic index.  Based on your description, you are going to
> be seq scanning your table a lot.  Focus on making it as small as
> possible, but vertical partitioning it so that the not-for-sale
> entries are hived off to an historical table, and horizontally
> partitioning it so that large columns rarely used in the where clause
> are in a separate table (Ideally you would tell postgresql to
> aggressively toast those columns, but there is no knob with which to
> do that)
>
>
>> 3. I expect that it does not make sense to index all columns?
>
> You mean individually, or jointly?  Either way, probably not.
>
>> 4. I expect it does not make sense to index boolean columns?
>
> In some cases it can, for example if the data distribution is very
> lopsided and the value with the smaller side is frequently specified.
>
>> 5. Is it better to do a combined index on 5 frequently used columns rather than having individual indexes on each of
them?
>
> How often are the columns specified together?  If they are completely
> independent it probably makes little sense to index them together.
>
>> 6. Would it be a goof idea to have all indexes sorted by my default sorting?
>
> You don't get to choose.  An btree index is sorted by the columns
> specified in the index, according to the operators specified (or
> defaulted).  Unless you mean that you want to add the default sort
> column to be the lead column in each index, that actually might make
> sense.
>
>> 7. Do you have so experiences with other approaches that could greatly improve performance (e.g. forcing indexes to
stayin memory etc.)? 
>
> If your queries are as unstructured as you imply, I'd forget about
> indexes for the most part, as you will have a hard time findings ones
> that work.  Concentrate on making seq scans as fast as possible.  If
> most of your queries end in something like "ORDER by price limit 10"
> then concentrate on index scans over price.  You will probably want to
> include heuristics in your UI such that if people configure queries to
> download half your database, you disallow that.  You will probably
> find that 90% of the workload comes from people who are just playing
> around with your website and don't actually intend to do business with
> you.
>
> Cheers,
>
> Jeff