Skip to main content

Getting following error: mount: you must specify the filesystem type in EC2

$df -h

Filesystem             Size   Used  Avail Use% Mounted on
/dev/xvda1             8.0G   1.1G   6.9G  14% /
tmpfs                  298M      0   298M   0% /dev/shm



[ec2-user@ip-xxx-xxx-xxx-xxx my-data]$ sudo su -

[root@ip-xxx-xxx-xxx-xxx ~]# fdisk -l

Disk /dev/xvda1: 8589 MB, 8589934592 bytes
255 heads, 63 sectors/track, 1044 cylinders
Units = cylinders of 16065 * 512 = 8225280 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disk identifier: 0x00000000


Disk /dev/xvdf: 1073 MB, 1073741824 bytes
255 heads, 63 sectors/track, 130 cylinders
Units = cylinders of 16065 * 512 = 8225280 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disk identifier: 0x76167e64

    Device Boot      Start         End      Blocks   Id  System
/dev/xvdf1               1         130     1044193+  8e  Linux LVM


[root@ip-xxx-xxx-xxx-xxx ~]# mkfs.ext3 /dev/sdf

mke2fs 1.42.3 (14-May-2012)
Filesystem label=
OS type: Linux
Block size=4096 (log=2)
Fragment size=4096 (log=2)
Stride=0 blocks, Stripe width=0 blocks
65536 inodes, 262144 blocks
13107 blocks (5.00%) reserved for the super user
First data block=0
Maximum filesystem blocks=268435456
8 block groups
32768 blocks per group, 32768 fragments per group
8192 inodes per group
Superblock backups stored on blocks:
        32768, 98304, 163840, 229376

Allocating group tables: done
Writing inode tables: done
Creating journal (8192 blocks): done
Writing superblocks and filesystem accounting information: done


[ec2-user@ip-xxx-xxx-xxx-xxx my-data]$ sudo mount /dev/sdf /mnt/my-data

[ec2-user@ip-xxx-xxx-xxx-xxx my-data]$ df -h
Filesystem            Size  Used Avail Use% Mounted on
/dev/xvda1            7.9G  967M  6.9G  13% /
tmpfs                 298M     0  298M   0% /dev/shm
/dev/xvdf            1008M   34M  924M   4% /mnt/my-data



Labels:

Comments

Gaurav Kanthed said…
it helped me to create attach EBS volume.
Tuesday, December 17, 2013
Gaurav Kanthed said…
it helped me a lot to attached EBS volume to my instance .

Happy Learning !
Tuesday, December 17, 2013
Post a Comment

Popular posts from this blog

What is the difference between Elastic and Enterprise Redis w.r.t "Hybrid Query" capabilities

  We'll explore scenarios involving nested queries, aggregations, custom scoring, and hybrid queries that combine multiple search criteria. 1. Nested Queries ElasticSearch Example: ElasticSearch supports nested documents, which allows for querying on nested fields with complex conditions. Query: Find products where the product has a review with a rating of 5 and the review text contains "excellent". { "query": { "nested": { "path": "reviews", "query": { "bool": { "must": [ { "match": { "reviews.rating": 5 } }, { "match": { "reviews.text": "excellent" } } ] } } } } } Redis Limitation: Redis does not support nested documents natively. While you can store nested structures in JSON documents using the RedisJSON module, querying these nested structures with complex condi...
Post a Comment
Read more

How are vector databases used?

  Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
Post a Comment
Read more

Feature Engineering - What and Why

Feature engineering is a crucial step in the machine learning pipeline where you create new, meaningful features or transform existing features to improve the performance of your predictive models. It involves selecting, modifying, or creating features from your raw data to make it more suitable for machine learning algorithms. Here's a more detailed overview of feature engineering: Why Feature Engineering? Feature engineering is essential for several reasons: Improving Model Performance: Well-engineered features can significantly boost the predictive power of your machine learning models. Handling Raw Data: Raw data often contains noise, missing values, and irrelevant information. Feature engineering helps in cleaning and preparing the data for analysis. Capturing Domain Knowledge: Domain-specific insights can be incorporated into feature creation to make the model more representative of the problem. Common Techniques and Strategies: 1. Feature Extraction: Transforming raw data...
Post a Comment
Read more