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https://issues.apache.org/jira/browse/SOLR-665?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12617654#action_12617654
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funtick edited comment on SOLR-665 at 7/28/08 8:48 PM:
-----------------------------------------------------------
bq. The Solr admin pages will not give you exact measurements.
Yes, and I do not need exact measurements! It gives me averageTimePerRequest
which improved almost 10 times on production server. Should I right JUnit tests
and execute it in a single-threaded environment? Better is to use The Grinder,
but I don't have time and spare CPUs.
bq. I've seen throughputs in excess of 400 searches per second.
But 'searches per second' is not the same as 'average response time'!!!
bq. Are you using highlighting or anything else that might be CPU-intensive at
all?
Yes, I am using highlighting. You can see it at http://www.tokenizer.org
bq. I'm guessing that you're caching the results of all queries in memory such
that no disk access is necessary.
{color:red} But this is another bug of SOLR!!! I am using extremely large
caches but SOLR still *recalculates* facet intersections. {color}
bq. A FIFO cache might become a bottleneck itself - if the cache is very large
and the most frequently accessed item is inserted just after the cache is
created, all accesses will need to traverse all the other entries before
getting that item.
- sorry, I didn't understand... yes, if cache contains 100000 entries and 'most
popular item' is removed... Why 'traverse all the other entries before getting
that item'? why 99999 items are less popular (cumulative) than single one
(absolute)?
You probably mean 'LinkedList traversal' but this is not the case. This is why
we need to browse JavaSource... LinkedHashMap extends HashMap and there is no
any 'traversal',
{code}
public V get(Object key) {
Entry<K,V> e = (Entry<K,V>)getEntry(key);
if (e == null)
return null;
e.recordAccess(this);
return e.value;
}
{code}
bq. Consider the following case: thread A performs a synchronized put, thread B
performs an unsynchronized get on the same key. B gets scheduled before A
completes, the returned value will be undefined.
the returned value is well defined: it is either null or correct value.
bq. That's exactly the case here - the update thread modifies the map
structurally!
Who cares? We are not iterating the map!
bq. That said, if you can show conclusively (e.g. with a profiler) that the
synchronized access is indeed the bottleneck and incurs a heavy penalty on
performance, then I'm all for investigating this further.
*What?!!*
Anyway, I believe simplified ConcurrentLRU backed by ConcurrentHashMap is
easier to understand and troubleshoot...
bq. I don't see the point of the static popularityCounter... that looks like a
bug.
No, it is not a bug. it is virtually "checkpoint", like as a timer, one timer
for all instances. We can use System.currentTimeMillis() instead, but static
volatile long is faster.
About specific use case: yes... if someone has 0.5 seconds response time for
faceted queries I am very happy... I had 15 seconds before going with FIFO.
was (Author: funtick):
bq. The Solr admin pages will not give you exact measurements.
Yes, and I do not need exact measurements! It gives me averageTimePerRequest
which improved almost 10 times on production server. Should I right JUnit tests
and execute it in a single-threaded environment? Better is to use The Grinder,
but I don't have time and spare CPUs.
bq. I've seen throughputs in excess of 400 searches per second.
But 'searches per second' is not the same as 'average response time'!!!
bq. Are you using highlighting or anything else that might be CPU-intensive at
all?
Yes, I am using highlighting. You can see it at http://www.tokenizer.org
bq. I'm guessing that you're caching the results of all queries in memory such
that no disk access is necessary.
{color:red} But this is another bug of SOLR!!! I am using extremely large
caches but SOLR still *recalculates* facet intersections. {color}
bq. A FIFO cache might become a bottleneck itself - if the cache is very large
and the most frequently accessed item is inserted just after the cache is
created, all accesses will need to traverse all the other entries before
getting that item.
- sorry, I didn't understand... yes, if cache contains 100000 entries and 'most
popular item' is removed... Why 'traverse all the other entries before getting
that item'? why 99999 items are less popular (cumulative) than single one
(absolute)?
You probably mean 'LinkedList traversal' but this is not the case. This is why
we need to browse JavaSource... LinkedHashMap extends HashMap and there is no
any 'traversal',
{code}
public V get(Object key) {
Entry<K,V> e = (Entry<K,V>)getEntry(key);
if (e == null)
return null;
e.recordAccess(this);
return e.value;
}
{code}
bq. Consider the following case: thread A performs a synchronized put, thread B
performs an unsynchronized get on the same key. B gets scheduled before A
completes, the returned value will be undefined.
the returned value is well defined: it is either null or correct value.
bq. That's exactly the case here - the update thread modifies the map
structurally!
Who cares? We are not iterating the map!
Anyway, I believe simplified ConcurrentLRU backed by ConcurrentHashMap is
easier to understand and troubleshoot...
bq. I don't see the point of the static popularityCounter... that looks like a
bug.
No, it is not a bug. it is virtually "checkpoint", like as a timer, one timer
for all instances. We can use System.currentTimeMillis() instead, but static
volatile long is faster.
About specific use case: yes... if someone has 0.5 seconds response time for
faceted queries I am very happy... I had 15 seconds before going with FIFO.
> FIFO Cache (Unsynchronized): 9x times performance boost
> -------------------------------------------------------
>
> Key: SOLR-665
> URL: https://issues.apache.org/jira/browse/SOLR-665
> Project: Solr
> Issue Type: Improvement
> Affects Versions: 1.3
> Environment: JRockit R27 (Java 6)
> Reporter: Fuad Efendi
> Attachments: FIFOCache.java
>
> Original Estimate: 672h
> Remaining Estimate: 672h
>
> Attached is modified version of LRUCache where
> 1. map = new LinkedHashMap(initialSize, 0.75f, false) - so that
> "reordering"/true (performance bottleneck of LRU) is replaced to
> "insertion-order"/false (so that it became FIFO)
> 2. Almost all (absolutely unneccessary) synchronized statements commented out
> See discussion at
> http://www.nabble.com/LRUCache---synchronized%21--td16439831.html
> Performance metrics (taken from SOLR Admin):
> LRU
> Requests: 7638
> Average Time-Per-Request: 15300
> Average Request-per-Second: 0.06
> FIFO:
> Requests: 3355
> Average Time-Per-Request: 1610
> Average Request-per-Second: 0.11
> Performance increased 9 times which roughly corresponds to a number of CPU in
> a system, http://www.tokenizer.org/ (Shopping Search Engine at Tokenizer.org)
> Current number of documents: 7494689
> name: filterCache
> class: org.apache.solr.search.LRUCache
> version: 1.0
> description: LRU Cache(maxSize=10000000, initialSize=1000)
> stats: lookups : 15966954582
> hits : 16391851546
> hitratio : 0.102
> inserts : 4246120
> evictions : 0
> size : 2668705
> cumulative_lookups : 16415839763
> cumulative_hits : 16411608101
> cumulative_hitratio : 0.99
> cumulative_inserts : 4246246
> cumulative_evictions : 0
> Thanks
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