kandi X-RAY | disk Summary
kandi X-RAY | disk Summary
使用java做的一个网盘系统，实现上传，下载，删除，分享等功能。 数据库: /* SQLyog Professional v12.09 (64 bit) MySQL - 5.6.21 : Database - drive. /*!40101 SET NAMES utf8 */;. /!40014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0 /; /!40014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0 /; /!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='NO_AUTO_VALUE_ON_ZERO' /; /!40111 SET @OLD_SQL_NOTES=@@SQL_NOTES, SQL_NOTES=0 /; CREATE DATABASE /!32312 IF NOT EXISTS/drive /*!40100 DEFAULT CHARACTER SET utf8 */;. /*Table structure for table catalog */. DROP TABLE IF EXISTS catalog;. CREATE TABLE catalog ( cId varchar(50) NOT NULL, pId varchar(50) DEFAULT NULL, cName varchar(50) DEFAULT NULL, cDate varchar(50) DEFAULT NULL, cF varchar(50) DEFAULT NULL, isShare varchar(2) DEFAULT NULL, PRIMARY KEY (cId) ) ENGINE=InnoDB DEFAULT CHARSET=utf8;. /*Table structure for table catalog_file */. DROP TABLE IF EXISTS catalog_file;. CREATE TABLE catalog_file ( cf varchar(50) NOT NULL, fid varchar(50) DEFAULT NULL, KEY cf (cf) ) ENGINE=InnoDB DEFAULT CHARSET=latin1;. /*Table structure for table file */. DROP TABLE IF EXISTS file;. CREATE TABLE file ( fId varchar(50) NOT NULL, fPath text, fSize int(50) DEFAULT NULL, fType varchar(50) DEFAULT NULL, fName varchar(50) DEFAULT NULL, fHash varchar(50) DEFAULT NULL, fDowncount int(11) DEFAULT NULL, fDesc varchar(50) DEFAULT NULL, fUploadtime date DEFAULT NULL, isShare bigint(2) DEFAULT NULL, cId varchar(50) DEFAULT NULL, fDiskName varchar(50) DEFAULT NULL, PRIMARY KEY (fId) ) ENGINE=InnoDB DEFAULT CHARSET=utf8;. /*Table structure for table info */. DROP TABLE IF EXISTS info;. CREATE TABLE info ( iId varchar(50) NOT NULL, iTitle varchar(100) DEFAULT NULL, iContent text, iTime varchar(50) DEFAULT NULL, iImage varchar(500) DEFAULT NULL, isImage int(11) DEFAULT NULL, iLocation int(11) DEFAULT NULL, iStart int(11) DEFAULT NULL, PRIMARY KEY (iId) ) ENGINE=InnoDB DEFAULT CHARSET=utf8;. /*Table structure for table role */. DROP TABLE IF EXISTS role;. CREATE TABLE role ( role varchar(20) DEFAULT NULL ) ENGINE=InnoDB DEFAULT CHARSET=latin1;. /*Table structure for table user */. DROP TABLE IF EXISTS user;. CREATE TABLE user ( uId varchar(55) NOT NULL, userName varchar(50) DEFAULT NULL, uPassword varchar(50) DEFAULT NULL, cId varchar(50) DEFAULT NULL, uTime varchar(50) DEFAULT NULL, role varchar(50) DEFAULT NULL, fileSize varchar(100) DEFAULT NULL, PRIMARY KEY (uId) ) ENGINE=InnoDB DEFAULT CHARSET=latin1;. /*!40101 SET SQL_MODE=@OLD_SQL_MODE /; /!40014 SET FOREIGN_KEY_CHECKS=@OLD_FOREIGN_KEY_CHECKS /; /!40014 SET UNIQUE_CHECKS=@OLD_UNIQUE_CHECKS /; /!40111 SET SQL_NOTES=@OLD_SQL_NOTES */;.
Top functions reviewed by kandi - BETA
- Handles POST request
- Creates the checksum from the given stream
- Generate a random id
- Get the MD5 checksum of an InputStream
- Get file header
- Encode filename
- Edit the database
- Edit count
- Creates a catalog
- Create a catalog
- Gets the isShare property
- Gets the parent catalog
- Delete file
- Delete by fidx id
- Find file by fid
- Find a file by its fid
- Delete catalog
- Find catalog by cid
- Delete by catalog
- Gets the catalog
- Find by cid
- Test the test
- Set the field id
- Update this upload status with the given number of bytes
- Find all files by column family
- Handle GET
disk Key Features
disk Examples and Code Snippets
Trending Discussions on disk
I have created a GCP service account with org viewer permissions (I assume therefore having read rights in all projects)...
ANSWERAnswered 2021-Jun-15 at 20:49
The error messages states that the service account does not have the permission
What permissions does the role
TL;DR: Interested in knowing if it's possible to use Abstract Base Classes as a mixin in the way I'd like to, or if my approach is fundamentally misguided.
I have a Flask project I've been working on. As part of my project, I've implemented a "RememberingDict" class. It's a simple subclass of dict, with a handful of extra features tacked on: it remembers its creation time, it knows how to pickle/save itself to a disk, and it knows how to open/unpickle itself from a disk:...
ANSWERAnswered 2021-Jun-15 at 03:43
You can get around the problems of subclassing
dict by subclassing
collections.UserDict instead. As the docs say:
Class that simulates a dictionary. The instance’s contents are kept in a regular dictionary, which is accessible via the data attribute of UserDict instances. If initialdata is provided, data is initialized with its contents; note that a reference to initialdata will not be kept, allowing it be used for other purposes.
Essentially, it's a thin regular-class wrapper around a
dict. You should be able to use it with multiple inheritance as an abstract base class, as you do with
I would like to find minimum distance of each voxel to a boundary element in a binary image in which the z voxel size is different from the xy voxel size. This is to say that a single voxel represents a 225x110x110 (zyx) nm volume.
Normally, I would do something with scipy.ndimage.morphology.distance_transform_edt (https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.morphology.distance_transform_edt.html) but this gives the assume that isotropic sizes of the voxel:...
ANSWERAnswered 2021-Jun-15 at 02:32
Normally, I would do something with scipy.ndimage.morphology.distance_transform_edt but this gives the assume that isotropic sizes of the voxel:
It does no such thing! You are looking for the
sampling= parameter. From the latest version of the docs:
Spacing of elements along each dimension. If a sequence, must be of length equal to the input rank; if a single number, this is used for all axes. If not specified, a grid spacing of unity is implied.
The wording "sampling" or "spacing" is probably a bit mysterious if you think of pixels as little squares/cubes, and that is probably why you missed it. In most situations, it is better to think of pixels as point samples on a grid, with fixed spacing between samples. I recommend Alvy Ray's a pixel is not a little square for a better understanding of this terminology.
I don't really know where the error is, for me, it's still a mystery. But I'm using Laravel 8 to produce a project, it was working perfectly and randomly started to return this error and all projects started to return this error too. I believe it's something with Redis, as I'm using it to store the system cache. When I go to access my endpoint in postman it returns the following error:...
ANSWERAnswered 2021-Jun-12 at 01:50
Your problem is that you have set
SESSION_CONNECTION=session, but your
SESSION_DRIVER=default, so you have to use
SESSION_DRIVER=database in your
.env. See the
I am using the ECK operator, to create an
The instance uses a
StorageClass that has
Retain (instead of
Delete) as its reclaim policy.
Here are my
PVCs before deleting the
ANSWERAnswered 2021-Jun-14 at 15:38
with the hope that due to the Retain policy, the new pods (i.e. their PVCs would bind to the existing PVs (and data wouldn't get lost)
It is explicitly written in the documentation that this is not what happens. the PVs are not available for another PVC after delete of a PVC.
the PersistentVolume still exists and the volume is considered "released". But it is not yet available for another claim because the previous claimant's data remains on the volume.
I get this most common error message in shiny app. I am well aware of this error and have resolved it dozens of time. But this time I am stumped....
ANSWERAnswered 2021-Apr-23 at 03:30
The problem seems to be in this line
I have master-slave (primary-standby) streaming replication set up on 2 physical nodes. Although the replication is working correctly and walsender and walreceiver both work fine, the files in the
pg_wal folder on the slave node are not getting removed. This is a problem I have been facing every time I try to bring the slave node back after a crash. Here are the details of the problem:
postgresql.conf on master and slave/standby node...
ANSWERAnswered 2021-Jun-14 at 15:00
You didn't describe omitting pg_replslot during your rsync, as the docs recommend. If you didn't omit it, then now your replica has a replication slot which is a clone of the one on the master. But if nothing ever connects to that slot on the replica and advances the cutoff, then the WAL never gets released to recycling. To fix you just need to shutdown the replica, remove that directory, restart it, (and wait for the next restart point to finish).
Do they need to go to wal_archive folder on the disk just like they go to wal_archive folder on the master node?
No, that is optional not necessary. It is set by
archive_mode = always if you want it to happen.
I have four questions. Suppose in spark I have 3 worker nodes. Each worker node has 3 executors and each executor has 3 cores. Each executor has 5 gb memory. (Total 6 executors, 27 cores and 15gb memory). What will happen if:
I have 30 data partitions. Each partition is of size 6 gb. Optimally, the number of partitions must be equal to number of cores, since each core executes one partition/task (One task per partition). Now in this case, how will each executor-core will process the partition since partition size is greater than the available executor memory? Note: I'm not calling cache() or persist(), it's simply that i'm applying some narrow transformations like map() and filter() on my rdd.
Will spark automatically try to store the partitions on disk? (I'm not calling cache() or persist() but merely just transformations are happening after an action is called)
Since I have partitions (30) greater than the number of available cores (27) so at max, my cluster can process 27 partitions, what will happen to the remaining 3 partitions? Will they wait for the occupied cores to get freed?
If i'm calling persist() whose storage level is set to MEMORY_AND_DISK, then if partition size is greater than memory, it will spill data to the disk? On which disk this data will be stored? The worker node's external HDD?
ANSWERAnswered 2021-Jun-14 at 13:26
I answer as I know things on each part, possibly disregarding a few of your assertions:
I have four questions. Suppose in spark I have 3 worker nodes. Each worker node has 3 executors and each executor has 3 cores. Each executor has 5 gb memory. (Total 6 executors, 27 cores and 15gb memory). What will happen if: >>> I would use 1 Executor, 1 Core. That is the generally accepted paradigm afaik.
I have 30 data partitions. Each partition is of size 6 gb. Optimally, the number of partitions must be equal to number of cores, since each core executes one partition/task (One task per partition). Now in this case, how will each executor-core will process the partition since partition size is greater than the available executor memory? Note: I'm not calling cache() or persist(), it's simply that I'm applying some narrow transformations like map() and filter() on my rdd. >>> The number of partitions being the same of number of cores is not true. You can service 1000 partitions with 10 cores, processing one at a time. What if you have 100K partition and on-prem? Unlikely you will get 100K Executors. >>> Moving on and leaving Driver-side collect issues to one side: You may not have enough memory for a given operation on an Executor; Spark can spill to files to disk at the expense of speed of processing. However, the partition size should not exceed a maximum size, was beefed up some time ago. Using multi-core Executors failure can occur, i.e. OOM's, also a result of GC-issues, a difficult topic.
Will spark automatically try to store the partitions on disk? (I'm not calling cache() or persist() but merely just transformations are happening after an action is called) >>> Not if it can avoid it, but when memory is tight, eviction / spilling to disk can and will occur, and in some cases re-computation from source or last checkpoint will occur.
Since I have partitions (30) greater than the number of available cores (27) so at max, my cluster can process 27 partitions, what will happen to the remaining 3 partitions? Will they wait for the occupied cores to get freed? >>> They will be serviced by a free Executor at a point in time.
If I'm calling persist() whose storage level is set to MEMORY_AND_DISK, then if partition size is greater than memory, it will spill data to the disk? On which disk this data will be stored? The worker node's external HDD? >>> Yes, and it will be spilled to the local file system. I think you can configure for HDFS via a setting, but local disks are faster.
This an insightful blog: https://medium.com/swlh/spark-oom-error-closeup-462c7a01709d
I'm using the Ruby SDK for AWS ECS to kick-off a task hosted in Fargate via
run_task method. This all works fine with the defaults — I can kick off the task OK and can send along custom command parameters to my Docker container:
ANSWERAnswered 2021-Jun-14 at 09:28
This was a bug of the SDK, now fixed (server-side, so doesn't require a library update).
The block of code in the question is the correct way for increasing ephemeral storage via the Ruby SDK:
I need to upload a v8 heap dump into an AWS S3 bucket after it's generated however the file that is uploaded is either 0KB or 256KB. The file on the server is over 70MB in size so it appears that the request isn't waiting until the heap dump isn't completely flushed to disk. I'm guessing the readable stream that is getting piped into
fs.createWriteStream is happening in an async manner and the
await with the call to the function isn't actually waiting. I'm using the v3 version of the AWS NodeJS SDK. What am I doing incorrectly?
ANSWERAnswered 2021-Jun-14 at 03:53
Your guess is correct. The
createHeapSnapshot() returns a promise, but that promise has NO connection at all to when the stream is done. Therefore, when the caller uses
await on that promise, the promise is resolved long before the stream is actually done.
async functions have no magic in them to somehow know when a non-promisified asynchronous operation like
.pipe() is done. So, your
async function returns a promise that has no connection at all to the stream functions.
Since streams don't have very much native support for promises, you can manually promisify the completion and errors of the streams:
No vulnerabilities reported
You can use disk like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the disk component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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