Hello!
I have an SSD that has been used previously with Aerospike. I want to re-use it in a new Aerospike set up.
I used parted to create a new GPT label, then new partition, then formatted the partition as EXT4. Then I deleted the partition, and used the SSD in Aerospike with a new previously unused namespace name.
When I started up my new Aerospike config, before I inserted any records, I have the old sets & data, but showing up in the new namespace!
I do appreciate data persistence, but this is going a bit too far!
How can I wipe the SSD so Aerospike does not see old sets, and records?
EDIT: I found this (https://www.aerospike.com/docs/operations/plan/ssd/ssd_init.html) finally. So the way to prepare a used disk, per the Aerospike developers, is to do a full disk write. I decided to do a secure erase via the Linux nvme command instead, and that got rid of the old data. IDK which solution is worse for the SSD…
Old aerospike.conf:
namespace classifier {
replication-factor 1
memory-size 4G
storage-engine memory
}
namespace classifier_ssd {
memory-size 2G
storage-engine device {
device /dev/nvme2n1
write-block-size 128K
}
}
Data in the old classifier_ssd namespace:
aql> show sets
+------------------+------------------+---------+-------------------+----------------------+-------------------+--------------+------------+
| disable-eviction | ns | objects | stop-writes-count | set | memory_data_bytes | truncate_lut | tombstones |
+------------------+------------------+---------+-------------------+----------------------+-------------------+--------------+------------+
| "false" | "classifier" | "0" | "0" | "classified_files" | "0" | "0" | "0" |
| "false" | "classifier_ssd" | "5" | "0" | "trained_model_list" | "0" | "0" | "0" |
| "false" | "classifier_ssd" | "3" | "0" | "demo_set2" | "0" | "0" | "0" |
+------------------+--------------+---------+-------------------+----------------------+-------------------+--------------+------------+
[127.0.0.1:3000] 3 rows in set (0.001 secs)
OK
aql> select * from classifier_ssd.trained_model_list
+------------+-------------------+-------------+-------------+-------------+-------------+
| test-bin-1 | test-bin-2 | prediction1 | prediction2 | prediction3 | prediction4 |
+------------+-------------------+-------------+-------------+-------------+-------------+
| 3 | "3" | | | | |
| 1 | "1" | | | | |
| 1234 | "test-bin-2-data" | 0.85 | 0.09 | 0.01 | 0.005 |
| 2 | "2" | | | | |
| 4 | "4" | | | | |
+------------+-------------------+-------------+-------------+-------------+-------------+
5 rows in set (0.066 secs)
OK
New aerospike.conf:
namespace classifier {
replication-factor 1
memory-size 4G
storage-engine memory
}
namespace ssd_backed {
memory-size 2G
default-ttl 0
storage-engine device {
device /dev/nvme2n1
write-block-size 128K
}
}
Data in the new ssd_backed namespace:
Note the objects count!!
aql> show sets
+------------------+--------------+---------+-------------------+----------------------+-------------------+--------------+------------+
| disable-eviction | ns | objects | stop-writes-count | set | memory_data_bytes | truncate_lut | tombstones |
+------------------+--------------+---------+-------------------+----------------------+-------------------+--------------+------------+
| "false" | "classifier" | "0" | "0" | "classified_files" | "0" | "0" | "0" |
| "false" | "ssd_backed" | "5" | "0" | "trained_model_list" | "0" | "0" | "0" |
| "false" | "ssd_backed" | "3" | "0" | "demo_set2" | "0" | "0" | "0" |
+------------------+--------------+---------+-------------------+----------------------+-------------------+--------------+------------+
[127.0.0.1:3000] 3 rows in set (0.001 secs)
OK
aql> select * from ssd_backed.trained_model_list
+------------+-------------------+-------------+-------------+-------------+-------------+
| test-bin-1 | test-bin-2 | prediction1 | prediction2 | prediction3 | prediction4 |
+------------+-------------------+-------------+-------------+-------------+-------------+
| 3 | "3" | | | | |
| 1 | "1" | | | | |
| 1234 | "test-bin-2-data" | 0.85 | 0.09 | 0.01 | 0.005 |
| 2 | "2" | | | | |
| 4 | "4" | | | | |
+------------+-------------------+-------------+-------------+-------------+-------------+
5 rows in set (0.066 secs)
OK