Does anyone know what is the maximum size of Item payload that amazon dynamo DB supports? I am sure its buried in documentation somewhere.
My follow-up question is that when you upload large chunk of data if there is a connection drop (client or server), is there a way to resume the upload from where you left off?
The maximum size of a DynamoDB item is 400KB.
From the Limits in DynamoDB documentation:
The maximum item size in DynamoDB is 400 KB, which includes both attribute name binary length (UTF-8 length) and attribute value lengths (again binary length). The attribute name counts towards the size limit.
This is what i could figure from documentation
Unlimited attributes /item
Unlimited item /table
400KB max /attribute
- 64KB max /item name [Edited per documentation --an item name must be at least one character long, but not greater than 64 KB long.]
Large data needs to be stored in Amazon S3 with url pointing to the data ?
Related
In AWS documentation, it stated that
"For provisioned mode tables, you specify throughput capacity in terms of read capacity units (RCUs) and write capacity units (WCUs):
One read capacity unit represents **one strongly consistent read per second**, or two eventually consistent reads per second, for an item up to 4 KB in size."
But what count as one read? If I loop through different partitions to read from dynamodb, will each loop count as one read? Thank you.
Reference: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html
For a GetItem and BatchGetItem operation which read an individual item, the size of the entire item is used to calculate the amount of RCU (read capacity units) used, even if you only ask to read specific parts from this item. As you quoted, this size is than rounded up to a multiple of 4K: If the item is 3.9K you'll pay one RCU for a strongly-consistent read (ConsistentRead=true), and two RCUs for a 4.1K item. Again, as you quoted, if you asked for an eventual-consistent read (ConsistentRead=false) the number of RCUs would be halved.
For transactions (TransactGetItems) the number of RCUs is double what it would have been with consistent reads.
For scans - Scan or Query - the cost is calculated the same as reading a single item, except for one piece of good news: The rounding up happens for the entire size read, not for each individual item. This is very important for small items - for example consider that you have items of 100 bytes each. Reading each one individually costs you one RCU even though it's only 100 bytes, not 4K. But if you Query a partition that has 40 of these items, the total size of these 40 items is 4000 bytes so you pay just one RCU to read all 40 items - not 40 RCUs. If the length of the entire partion is 4 MB, you'll pay 1024 RCUs when ConsistentRead=true, or 512 RCUs when ConsistentRead=false, to read the entire partition - regardless of how many items this partition contains.
I am currently doing a batch load to DynamoDB and dividing our data items into batch units:
According to the limits documentation:
https://docs.aws.amazon.com/amazondynamodb/latest/APIReference/API_BatchWriteItem.html
Some of the limits are:
There are more than 25 requests in the batch.
Any individual item in a batch exceeds 400 KB.
The total request size exceeds 16 MB.
The big unknown for me is how is possible with 25 items of a maximum of 400 Kb, the payload will exceed 16Mbs. Accounting for table names of less than 255 bytes, etc. I don't understand the limit or am I missing something simple.
Thanks.
The 16MB size is actually the total size of the request. Consider an object with many many small objects, the DynamoDB request map could be larger than the size of the combined items.
I have a question...
If I have 1000 item having same partition key in a table... And if I made a query for this partition key with limit 10 then I want to know does it take read capacity unit for 1000 items or for just 10 items
Please clear my doubt
I couldn't find the exact point in the DynamoDB documentation. From my experience it uses only the returned limit for consumed capacity which is 10 (Not 1000).
You can quickly evaluate this also using the following approach.
However, you can specify the ReturnConsumedCapacity parameter in
a Query request to obtain this information.
The limit option will limit the number of results returned. The capacity consumed depends on the size of the items, and how many of them are accessed (I say accessed because if you have filters in place, more capacity may be consumed than the number of items actually returned would consume if there are items that get filtered out) to produce the results returned.
The reason I mention this is because, for queries, each 4KB of returned capacity is equivalent to 1 read capacity unit.
Why is this important? Because if your items are small, then for each capacity unit consumed you could return multiple items.
For example, if each item is 200 bytes in size, you could be returning up to 20 items for each capacity unit.
According to the aws documentation:
The maximum number of items to evaluate (not necessarily the number of matching items). If DynamoDB processes the number of items up to the limit while processing the results, it stops the operation and returns the matching values up to that point, and a key in LastEvaluatedKey to apply in a subsequent operation, so that you can pick up where you left off.
It seems to me that it means that it will not consume the capacity units for all the items with the same partition key. According to your example the consumed capacity units will be for your 10 items.
However since I did not test it I cannot be sure, but that is how I understand the documentation.
what constitutes an actual read in DynamoDB?
is it reading every line in a table or what data is returned?
is this why a scan is so expensive - you read the entire table and are charged for every table line that is read?
Can you put ElasticCache (Memcached) in front of DynamoDB to keep the cost down?
Finally are you charged for a query that yields no results?
See this link: http://aws.amazon.com/dynamodb/faqs/
1 Write = 1 Write per second for an item up to 1Kb in size.
1 Read = 2 Reads per second for an item up to 1Kb in size, or 1 per second if you required fully consistent results.
For example, if your items are 512 bytes and you need to read 100
items per second from your table, then you need to provision 100 units
of Read Capacity.
If your items are larger than 1KB in size, then you should calculate
the number of units of Read Capacity and Write Capacity that you need.
For example, if your items are 1.5KB and you want to do 100
reads/second, then you would need to provision 100 (read per second) x
2 (1.5KB rounded up to the nearest whole number) = 200 units of Read
Capacity.
Note that the required number of units of Read Capacity is determined
by the number of items being read per second, not the number of API
calls. For example, if you need to read 500 items per second from your
table, and if your items are 1KB or less, then you need 500 units of
Read Capacity. It doesn’t matter if you do 500 individual GetItem
calls or 50 BatchGetItem calls that each return 10 items.
The above applies to all the usual methods, GET, BATCH X & QUERY.
SCAN is a little different, they don't document exactly how they calculate the usage but they do offer the following:
The Scan API will iterate through your entire dataset and apply the
filter conditions to every row. Since only 1MB of data can be scanned
at a time, you may need to do multiple round trips (using a
continuation token) to complete the scan. Further, using this API may
consume much of your provisioned read throughput. Hence, this method
has limited scaling characteristics and we do not recommend that you
use it as a part of your application’s regular behavior.
So to answer your question directly: The calculation is made on what data is returned in all cases except for SCAN, where there isn't really any clear indication on how they charge. A query that yields no results will not cost you anything.
You can definitely set up a caching system infront of Dynamo, definitely recommend you look into that if you want to keep your reads down.
Hope that helps!
I have read their limits FAQ, they talk about many limits except limit of the whole database.
This is fairly easy to deduce from the implementation limits page:
An SQLite database file is organized as pages. The size of each page is a power of 2 between 512 and SQLITE_MAX_PAGE_SIZE. The default value for SQLITE_MAX_PAGE_SIZE is 32768.
...
The SQLITE_MAX_PAGE_COUNT parameter, which is normally set to 1073741823, is the maximum number of pages allowed in a single database file. An attempt to insert new data that would cause the database file to grow larger than this will return SQLITE_FULL.
So we have 32768 * 1073741823, which is 35,184,372,056,064 (35 trillion bytes)!
You can modify SQLITE_MAX_PAGE_COUNT or SQLITE_MAX_PAGE_SIZE in the source, but this of course will require a custom build of SQLite for your application. As far as I'm aware, there's no way to set a limit programmatically other than at compile time (but I'd be happy to be proven wrong).
It has new limits, now the database size limit is 256TB:
Every database consists of one or more "pages". Within a single database, every page is the same size, but different databases can have page sizes that are powers of two between 512 and 65536, inclusive. The maximum size of a database file is 4294967294 pages. At the maximum page size of 65536 bytes, this translates into a maximum database size of approximately 1.4e+14 bytes (281 terabytes, or 256 tebibytes, or 281474 gigabytes or 256,000 gibibytes).
This particular upper bound is untested since the developers do not have access to hardware capable of reaching this limit. However, tests do verify that SQLite behaves correctly and sanely when a database reaches the maximum file size of the underlying filesystem (which is usually much less than the maximum theoretical database size) and when a database is unable to grow due to disk space exhaustion.
The new limit is 281 terabytes. https://www.sqlite.org/limits.html
Though this is an old question, but let me share my findings for people who reach this question.
Although Sqlite documentation states that maximum size of database file is ~140 terabytes but your OS imposes its own restrictions on maximum file size for any type of file.
For e.g. if you are using FAT32 disk on Windows, maximum file size that I could achieve for sqLite db file was 2GB. (According to Microsoft site, limit on FAT 32 system is 4GB but still my sqlite db size was restricted to 2GB). While on Linux , I was able to reach 3 GB (where I stopped. it could have reached more size)
NOTE: I had written a small java program that will start populating sqlite db from 0 rows and go on populating until stop command is given.
The maximum number of bytes in a string or BLOB in SQLite is defined by the preprocessor macro SQLITE_MAX_LENGTH. The default value of this macro is 1 billion (1 thousand million or 1,000,000,000).
The current implementation will only support a string or BLOB length up to 231-1 or 2147483647
The default setting for SQLITE_MAX_COLUMN is 2000. You can change it at compile time to values as large as 32767. On the other hand, many experienced database designers will argue that a well-normalized database will never need more than 100 columns in a table.
SQLite does not support joins containing more than 64 tables.
The theoretical maximum number of rows in a table is 2^64 (18446744073709551616 or about 1.8e+19). This limit is unreachable since the maximum database size of 140 terabytes will be reached first.
Max size of DB : 140 terabytes
Please check URL for more info : https://www.sqlite.org/limits.html
I'm just starting to explore SQLite for a project I'm working on, but it seems to me that the effective size of a database is actually more flexible than the file system would seem to allow.
By utilizing the 'attach' capability, a database could be compiled that would exceed the file system's max file size by up to 125 times... so a FAT32 effective limit would actually be 500GB (125 x 4GB)... if the data could be balanced perfectly between the various files.