Hello,
I would like to ask you a question regarding the conversion from a mondgodb to rosbag.
I am storing a number of topics (34 to be precise) into a mongodb using the following scenario file:
context: "default"
storage:
method: "database"
config: "default"
data: {
topics: {
topic_1: "topic_1_ros",
topic_2: "topic_2_ros",
...
topic_34: "topic_34_ros"
}
}
collection:
method: "timer"
timer_delay: 1
The specific scenario and the configuration with the timer of 1 second delay generates a lot of data and documents in the DB (something like 150 GBs for 24 hours of recording).
I want to pick some and convert them to rosbag, based on specific timestamps, i.e. take the items from the DB between 14:00 and 15:00 in that day. To do that I am using the convert.py rosnode with a query like that as argument
-q '{"_ts_meta.sys_time": {"$gt": 1680009972.091473, "$lt": 1680009992.091473}}'
The problem with this is that it takes way too much time and most of the time resources to execute this query and generate the corresponding bag file, and I was wondering if this is due to the big amount of data that is stored in the DB or the fact that it is deployed in a docker container or if it's something else that I am missing or doing wrong.
I would really appreciate some help to figure out what is the issue there.
Thank you
Hello,
I would like to ask you a question regarding the conversion from a mondgodb to rosbag.
I am storing a number of topics (34 to be precise) into a mongodb using the following scenario file:
The specific scenario and the configuration with the timer of 1 second delay generates a lot of data and documents in the DB (something like 150 GBs for 24 hours of recording).
I want to pick some and convert them to rosbag, based on specific timestamps, i.e. take the items from the DB between 14:00 and 15:00 in that day. To do that I am using the convert.py rosnode with a query like that as argument
The problem with this is that it takes way too much time and most of the time resources to execute this query and generate the corresponding bag file, and I was wondering if this is due to the big amount of data that is stored in the DB or the fact that it is deployed in a docker container or if it's something else that I am missing or doing wrong.
I would really appreciate some help to figure out what is the issue there.
Thank you