昨天负责的一个项目突然爆“out of memory for query result”。 背景 项目的数据表是保存超过10m的文本数据,通过json方式保存进postgres中,上传一个13m的大文件处理过程中出错。 怀疑 1 .celery进程过多 一开始怀疑celery进程过多导致的内存不足引起,查了一个有46个celery进程, 改为5个worker,状况没得到改善。
昨天负责的一个项目突然爆“out of memory for query result”。 背景 项目的数据表是保存超过10m的文本数据,通过json方式保存进postgres中,上传一个13m的大文件处理过程中出错。 怀疑 1 .celery进程过多 一开始怀疑celery进程过多导致的内存不足引起,查了一个有46个celery进程, 改为5个worker,状况没得到改善。
that the data is progressively fetch and not all at once put into memory, isn't it? Now I do have to manually run the query multiple times using LIMIT/OFFSET (manually adapted to the amount of RAM of the host machine...). Timo Re: OutOfMemory From "Wagner,Harry" Date: 29 March 2...
目前flink通过一个静态类来创建相相应的jdbc catalog,对于PostgresCatalog,没有提供public类型的构造方法。 通过JdbcCatalogUtils.createCatalog构造PostgresCatalog时这五个参数都是必填项,其中baseUrl要求是不能带有数据库名的 String catalogName = "mycatalog"; String defaultDatabase = "postgres"; String username =...
I've tried any work_mem value from 1gb all the way up to 40gb, with no effect on the error. I'd like to think of this problem as a server process memory (not the server's buffers) or client process memory issue, primarily because when we tested the error there was no other load...
System.OutOfMemoryException(Microsoft SQL Server Report Builder 3.0 Product version 11.0.3000.0) System.Web.Services.Protocols.SoapException Table alias ignored for columns specified in INSERT statement in SQL Server Table Explaination for dbo.catalog table in ReportServer database Table Value Expression ...
For Postgres: In the Postgres log, you see an error similar to: ERROR: integer out of range In vpxd logs, you see entries similar to: [date time] error vpxd[7F4AB1866700] [Originator@6876 sub=Default opID=HB-host-xxx@xxxxxx-xxxxxxxx] An unrecoverable problem has occurred, stopping the...
I still get out of memory exception using your code sample:复制 public static string ReturnResults(Results results) { JObject myJObject = JObject.FromObject(results); return myJObject.ToString(); } Tuesday, October 3, 2017 6:06 AMHi ganeshmuth,...
The problem: you’re loading all the data into memory at once. If you have enough rows in the SQL query’s results, it simply won’t fit in RAM. Pandas does have a batching option forread_sql(), which can reduce memory usage, but it’s still not perfect: it also loads all the ...
#maximum number of rpc memory pools rpc_msg_max=4096 app_exclude_cpus="0" 2.在另一台机器上启动启动tpcc测试 ./runBenchmark.sh ./props.pg cat props.pg db=postgres driver=org.postgresql.Driver conn=jdbc:postgresql://124.88.86.177:5432/tpcc1000?prepareThreshold=1&batchMode=on&fetchsize=10&...