I have an issue with Elasticsearch, at some times, it tries to run GC continously since this one is unable to free because heap size set to 14GB (min and max) is said to be completely allocated:
(...)
[2014-09-18 13:43:45,984][INFO ][monitor.jvm ] [staging02.onldev] [gc][old][1128185][65590] duration [7.1s], collections
[1]/[7.2s], total [7.1s]/[9.3h], memory [13.9gb]->[13.9gb]/[13.9gb], all_pools {[young] [532.5mb]->[532.5mb]/[532.5mb]}{[survivor] [
49.9mb]->[49.6mb]/[66.5mb]}{[old] [13.3gb]->[13.3gb]/[13.3gb]}
[2014-09-18 13:43:53,307][INFO ][monitor.jvm ] [staging02.onldev] [gc][old][1128186][65591] duration [7.2s], collections
[1]/[7.3s], total [7.2s]/[9.3h], memory [13.9gb]->[13.9gb]/[13.9gb], all_pools {[young] [532.5mb]->[532.5mb]/[532.5mb]}{[survivor] [
49.6mb]->[49.7mb]/[66.5mb]}{[old] [13.3gb]->[13.3gb]/[13.3gb]}
[2014-09-18 13:43:58,647][INFO ][monitor.jvm ] [staging02.onldev] [gc][old][1128187][65592] duration [5.2s], collections
[1]/[5.3s], total [5.2s]/[9.3h], memory [13.9gb]->[13.9gb]/[13.9gb], all_pools {[young] [532.5mb]->[532.5mb]/[532.5mb]}{[survivor] [
49.7mb]->[49.8mb]/[66.5mb]}{[old] [13.3gb]->[13.3gb]/[13.3gb]}
At this point ES is unresponsive, and we restart it
When i watch ES heap, and our application workers use ES, the heap memory grows, every few minutes the GC runs and the heap almost empties again, but not completely. And slowly over many days, there seems to be no memory in the heap available. It looks like if there was a memory leak, except how could it be in our Ruby code using Tire gem, since we are talking about ES heap ? Can some usage patterns of ES make ES leak memory ?
Basically, ES is a dedicated server with 16GB of RAM, no replicas, 5 indexes and 1 shard per index. It runs with java-1.7.0-openjdk-1.7.0.65-2.5.1.2.el6_5.x86_64, uses mlockall and min and max heap are both set to 14GB. Nothing else runs on the server. We use Elasticsearch 0.90.x because Dev team cannot afford replacing the Tire gem they use to connect in the Ruby workers
products
size: 164Mi (164Mi)
docs: 98,760 (157,138)
product_brands
size: 4.52Mi (4.52Mi)
docs: 5,123 (5,123)
product_categories
size: 358ki (358ki)
docs: 538 (538)
store_company_categories
size: 389ki (389ki)
docs: 4,028 (4,028)
stores
size: 1.44Mi (1.44Mi)
docs: 1,090 (1,090)
The biggest index is products, and shows up as 164MB in Bigdesk. How can ES use up until 14GB over time ?
Is there something wrong with the index metadata ?
{
state: open
settings: {
index.analysis.filter.french_stop.stopwords.0: alors
index.analysis.filter.french_stop.stopwords.1: au
index.analysis.filter.french_stop.stopwords.4: autre
index.analysis.filter.french_stop.stopwords.5: avant
index.analysis.filter.french_stop.stopwords.2: aucuns
index.analysis.filter.french_stop.stopwords.3: aussi
index.analysis.filter.french_stop.stopwords.22: dehors
index.analysis.filter.french_stop.stopwords.8: bon
index.analysis.filter.french_stop.stopwords.23: depuis
index.analysis.filter.french_stop.stopwords.9: car
index.analysis.filter.french_stop.stopwords.20: du
index.analysis.filter.french_stop.stopwords.6: avec
index.analysis.filter.french_stop.stopwords.21: dedans
index.analysis.filter.french_stop.stopwords.7: avoir
index.analysis.filter.french_stop.stopwords.29: droite
index.analysis.filter.french_stop.stopwords.28: dos
index.analysis.filter.french_stop.stopwords.27: donc
index.analysis.filter.french_stop.stopwords.26: doit
index.analysis.filter.french_stop.stopwords.25: devrait
index.analysis.filter.french_stop.stopwords.24: deux
index.analysis.analyzer.nGram_analyzer.type: custom
index.analysis.filter.nGram_filter.token_chars.0: letter
index.analysis.analyzer.product_analyzer.type: custom
index.analysis.filter.nGram_filter.token_chars.1: digit
index.analysis.filter.nGram_filter.token_chars.2: punctuation
index.analysis.filter.french_stemmer.type: stemmer
index.analysis.filter.nGram_filter.type: nGram
index.analysis.filter.french_stop.stopwords.10: ce
index.analysis.filter.french_stop.stopwords.11: cela
index.analysis.filter.french_stop.stopwords.12: ces
index.analysis.analyzer.product_analyzer.filter.0: lowercase
index.analysis.filter.french_stop.stopwords.91: sans
index.analysis.filter.french_stop.stopwords.18: dans
index.analysis.analyzer.product_analyzer.filter.1: french_stemmer
index.analysis.filter.french_stop.stopwords.92: ses
index.analysis.filter.french_stop.stopwords.17: comment
index.analysis.analyzer.product_analyzer.filter.2: asciifolding
index.analysis.analyzer.product_analyzer.filter.3: unique
index.analysis.filter.french_stop.stopwords.90: sa
index.analysis.filter.french_stop.stopwords.19: des
index.analysis.filter.french_stop.stopwords.14: chaque
index.analysis.analyzer.product_analyzer.filter.4: french_stop
index.analysis.filter.french_stop.stopwords.13: ceux
index.analysis.filter.nGram_filter.min_gram: 2
index.analysis.filter.french_stop.stopwords.16: comme
index.analysis.analyzer.category_analyzer.type: custom
index.analysis.filter.french_stop.stopwords.15: ci
index.analysis.filter.french_stop.stopwords.99: soyez
index.analysis.filter.french_stop.stopwords.97: sont
index.analysis.filter.french_stop.stopwords.98: sous
index.analysis.filter.french_stop.stopwords.95: sien
index.analysis.filter.french_stop.stopwords.96: son
index.analysis.filter.french_stop.stopwords.93: seulement
index.analysis.filter.french_stop.stopwords.94: si
index.analysis.analyzer.nGram_analyzer.tokenizer: whitespace
index.analysis.filter.french_stop.stopwords.80: plupart
index.analysis.filter.french_stop.stopwords.81: pour
index.number_of_replicas: 0
index.analysis.filter.french_stop.stopwords.82: pourquoi
index.analysis.filter.french_stop.stopwords.83: quand
index.analysis.filter.french_stop.stopwords.84: que
index.analysis.filter.french_stop.stopwords.85: quel
index.analysis.filter.french_stop.stopwords.86: quelle
index.analysis.filter.french_stop.stopwords.87: quelles
index.analysis.filter.french_stop.stopwords.88: quels
index.analysis.filter.french_stop.stopwords.89: qui
index.analysis.analyzer.product_analyzer.tokenizer: standard
index.analysis.filter.french_stop.stopwords.79: pièce
index.analysis.filter.french_stop.stopwords.70: ou
index.analysis.filter.french_stop.stopwords.73: parce
index.analysis.filter.french_stop.stopwords.74: parole
index.uuid: B_JF7UG5R6S_ZC0L0IMFYw
index.analysis.filter.french_stop.stopwords.71: où
index.analysis.filter.french_stop.stopwords.72: par
index.analysis.filter.french_stop.stopwords.77: peut
index.analysis.filter.french_stop.stopwords.78: peu
index.analysis.filter.french_stop.stopwords.75: pas
index.analysis.filter.french_stop.stopwords.76: personnes
index.analysis.filter.french_stop.stopwords.68: nous
index.analysis.filter.french_stop.stopwords.69: nouveaux
index.analysis.filter.french_stop.stopwords.65: ni
index.analysis.analyzer.category_analyzer.filter.0: lowercase
index.analysis.filter.french_stop.stopwords.64: même
index.analysis.filter.french_stop.stopwords.67: notre
index.analysis.filter.french_stop.stopwords.66: nommés
index.analysis.filter.french_stop.stopwords.61: moins
index.analysis.filter.french_stop.stopwords.60: mine
index.analysis.analyzer.category_analyzer.filter.1: french_stemmer
index.analysis.filter.french_stop.stopwords.63: mot
index.analysis.analyzer.category_analyzer.filter.2: french_stop
index.analysis.filter.french_stop.stopwords.62: mon
index.analysis.filter.french_stop.stopwords.120: ça
index.analysis.filter.french_stop.stopwords.121: étaient
index.analysis.filter.french_stop.stopwords.122: état
index.analysis.filter.french_stop.stopwords.123: étions
index.analysis.filter.french_stop.stopwords.124: été
index.analysis.filter.french_stop.stopwords.125: être
index.analysis.filter.nGram_filter.max_gram: 20
index.analysis.filter.french_stop.stopwords.126: rayon
index.analysis.filter.french_stop.stopwords.127: rayons
index.analysis.filter.french_stop.stopwords.128: root
index.number_of_shards: 1
index.analysis.filter.french_stop.stopwords.129: roots
index.analysis.filter.french_stop.stopwords.59: mes
index.analysis.filter.french_stop.stopwords.57: maintenant
index.analysis.filter.french_stop.stopwords.58: mais
index.analysis.filter.french_stop.stopwords.56: ma
index.analysis.filter.french_stop.stopwords.55: là
index.analysis.analyzer.whitespace_analyzer.tokenizer: whitespace
index.analysis.filter.french_stop.stopwords.54: leur
index.analysis.filter.french_stop.stopwords.53: les
index.analysis.filter.french_stop.stopwords.52: le
index.analysis.filter.french_stop.stopwords.51: la
index.analysis.analyzer.whitespace_analyzer.type: custom
index.analysis.filter.french_stop.stopwords.50: juste
index.analysis.analyzer.whitespace_analyzer.filter.1: french_stemmer
index.analysis.analyzer.whitespace_analyzer.filter.0: lowercase
index.analysis.filter.french_stop.type: stop
index.analysis.analyzer.whitespace_analyzer.filter.2: asciifolding
index.analysis.filter.french_stop.stopwords.114: voie
index.analysis.filter.french_stop.stopwords.115: voient
index.analysis.filter.french_stop.stopwords.112: tu
index.analysis.filter.french_stop.stopwords.113: valeur
index.analysis.filter.french_stop.stopwords.110: trop
index.analysis.filter.french_stop.stopwords.111: très
index.version.created: 901399
index.analysis.filter.french_stop.stopwords.46: ici
index.analysis.filter.french_stop.stopwords.47: il
index.analysis.filter.french_stop.stopwords.48: ils
index.analysis.filter.french_stop.stopwords.49: je
index.analysis.filter.french_stop.stopwords.118: vous
index.analysis.filter.french_stop.stopwords.119: vu
index.analysis.filter.french_stop.stopwords.116: vont
index.analysis.filter.french_stop.stopwords.117: votre
index.analysis.filter.french_stop.stopwords.41: fois
index.analysis.filter.nGram_filter.token_chars.3: symbol
index.analysis.filter.french_stop.stopwords.40: faites
index.analysis.analyzer.category_analyzer.tokenizer: standard
index.analysis.filter.french_stop.stopwords.43: force
index.analysis.filter.french_stop.stopwords.42: font
index.analysis.filter.french_stop.stopwords.45: hors
index.analysis.filter.french_stop.stopwords.44: haut
index.analysis.filter.french_stop.stopwords.101: sur
index.analysis.filter.french_stop.stopwords.102: ta
index.analysis.analyzer.nGram_analyzer.filter.3: nGram_filter
index.analysis.filter.french_stop.stopwords.103: tandis
index.analysis.analyzer.nGram_analyzer.filter.2: french_stemmer
index.analysis.filter.french_stop.stopwords.104: tellement
index.analysis.filter.french_stemmer.name: minimal_french
index.analysis.filter.french_stop.stopwords.100: sujet
index.analysis.filter.french_stop.stopwords.37: et
index.analysis.filter.french_stop.stopwords.109: tout
index.analysis.filter.french_stop.stopwords.38: eu
index.analysis.filter.french_stop.stopwords.35: essai
index.analysis.filter.french_stop.stopwords.36: est
index.analysis.analyzer.nGram_analyzer.filter.1: asciifolding
index.analysis.filter.french_stop.stopwords.105: tels
index.analysis.analyzer.nGram_analyzer.filter.0: lowercase
index.analysis.filter.french_stop.stopwords.106: tes
index.analysis.filter.french_stop.stopwords.39: fait
index.analysis.filter.french_stop.stopwords.107: ton
index.analysis.filter.french_stop.stopwords.108: tous
index.analysis.filter.french_stop.stopwords.30: début
index.analysis.filter.french_stop.stopwords.34: encore
index.analysis.filter.french_stop.stopwords.33: en
index.analysis.filter.french_stop.stopwords.32: elles
index.analysis.filter.french_stop.stopwords.31: elle
}
mappings: {
product_category: {
properties: {
tags: {
analyzer: category_analyzer
type: string
}
ancestry_path: {
type: string
}
name: {
analyzer: product_analyzer
type: string
}
leaf?: {
type: boolean
}
category_depth_0: {
properties: {
tags: {
type: string
}
name: {
analyzer: product_analyzer
type: string
}
}
}
name_suggest: {
index_analyzer: nGram_analyzer
search_analyzer: whitespace_analyzer
type: string
}
category_depth_3: {
properties: {
name: {
type: string
}
}
}
self_and_ancestors_ids: {
type: string
}
depth: {
type: integer
}
category_depth_1: {
properties: {
tags: {
type: string
}
name: {
analyzer: product_analyzer
type: string
}
}
}
category_depth_2: {
properties: {
tags: {
type: string
}
name: {
analyzer: product_analyzer
type: string
}
}
}
}
}
}
aliases: [ ]
}
I tried with using a min/max heap size of 6GB, but it exhibits the same behaviour, only becomes unresponsive sooner.
Problem solved:
Dev finally allowed me to update Elasticsearch to 1.3.2, Java changed to Oracle latest at this time, Ruby driver Tire replaced with
searchkick(because Dev said API was closer to Tire and official driver seemed too complicated to do a quick transition).With default configuration (no -Xmin and Xmax), Elasticsearch does not use more than 320MB heap and application is woaking as well as before. I will try setting Xmin and Xmax to a static value of 2GB to see if i see the same memory usage pattern as before.