Let's talk about what the real limitations are. There are two here that you
should care about:

1) Cassandra runs in the JVM. When you read and write to Cassandra, those
objects end up in the heap as byte arrays. If you're regularly reading and
writing 100MB byte arrays, it's easy to see situations where you'll have
some latency pains, especially if you have a lot of concurrent requests.
2) On the read path, we build up an index of CQL rows within a CQL
partition. You've been reading books, I suspect you know the difference (if
not, ask, and I'll re-explain). In all versions of cassandra released so
far, the cost of that index scales with the width of the partition and is
paid ON READ (not on write like other databases). If you have a very wide
CQL partition and you query it quickly, you will create JVM GC pressure. It
sounds like this is a secondary concern here.

That doesn't mean it's not a good fit. There are workarounds to both of
these issues.

For example:
- On the write path, running with offheap memtables will get the cell value
into direct memory for the period of time between when it's written in the
commitlog and when it's flushed to disk. This is likely important for you.
- Instead of writing the 100MB document in a single cell, chunk it into 1MB

CREATE TABLE documents (
document_id text,
chunk_order int,
chunk_id text,
PRIMARY KEY (document_id, chunk_order))

chunk_id text,
chunk blob,
PRIMARY KEY(chunk_id))

Then when you go to write the document, you break it into 1MB blobs, and
take the hash (md5, sha1, sha256, whatever suits your needs based on pain
of collisions),  write the chunk into the chunks table, and the chunk_id
into the documents table for the document (in the right order).

This does a few things:
1) You can reassemble the document chunk by chunk by querying it in pieces.
Each piece is small enough not to overwhelm the garbage collector (and you
control that with paging)
2) The only partition here that can get large is document_id, and it'd be
incredibly unlikely that you'll get 100MB per partition here based on your
description, so you dont have to worry about the index pain on the read path
3) You naturally dedup chunks, which you didnt ask for, but may care about.

Hope that helps,
- Jeff

On Sun, Jun 10, 2018 at 9:35 AM, Ralph Soika <ralph.so...@imixs.com> wrote:

> Thanks for your answer. Ok - I think I understand your points and the
> worries you have about my architecture.
> To give more inside information: We are working on the Open Source Project
> Imixs-Workflow <http://www.imixs.org>. This is a human-centric workflow
> engine based on Java EE. The engine runs on JPA/SQL Databases. This is to
> have full transactional support. We also use Lucene Search technology to
> find records in a very unstructured amount of business data.  Everything
> runs stable and fast (for example with PostgreSQL) - also if we have
> records containing 100MB of attachments.
> But we need also a stable archive strategy. Normal Backups are not really
> an option because of the fact that databases grow over the years and so we
> are seeking a Big Table solution. Cassandra seems much stronger in this
> area than traditional SQL solutions. And it seems to be easy to setup a
> cluster of 3 nodes. It is not easy to build the same with Hadoop.
> Our Cassandra approach is not for data live access. It is for an
> asynchronous archive service with the goal of an highly data consistence
> decentralized storage. And this is why I am not worried about performance.
> Only in case of an restore or a big-data analyses we are reading data from
> Cassandra.
> I can't change the fact that I have business transactions that contain
> files with more than 100MB of data.
> Do you really think Cassandra has less performance in writing/reading a
> 200MB media file than PostgreSQL? In my first test I have not. I have the
> concern that through some Internet discussion the impression is, that
> Cassandra is worse than a traditional SQL solution.  I thought Cassandra
> is basically a big-data solution??
> If Cassandra is not suitable to store records larger than 100MB, I ask if
> the only alternative would be HBase?
> To put it more clearly: it's always a challenge to handle a record with
> more than 100MB. But the question is: Does Cassandra break in this kind of
> task?
> So if we exclude the performance issue for a moment, would you agree to
> the solution or advise against it?
> Thanks again for you help
> ===
> Ralph
> Am 10.06.2018 um 17:43 schrieb daemeon reiydelle:
> I'd like to split your question into two parts.
> Part one is around recovery. If you lose a copy of the underlying data
> because a note fails and let's assume you have three copies, how long can
> you tolerate the time to restore the third copy?
> The second question is about the absolute length of a row. This question
> is more about the time to read a row if it's a single super long row, that
> can only be read from one node, if the row is split into multiple shorter
> rows then in most cases there is an opportunity to read it in parallel.
> The sizes you're looking at are not in themselves an issue, it's more how
> you want to access and use the data.
> I might argue that you might not want to use Cassandra, if this is your
> only use case for Cassandra. I might suggest you look at something like
> elk, whether or not you use elasticsearch or Cassandra might get you
> thinking about your architecture to meet this particular business case. But
> of course if you have multiple use cases to store something some tables or
> shorter columns and others, then overall Cassandra would be an excellent
> choice.
> But as is often the case, and I do hope I'm being helpful in this
> response, your overall family of business processes can drive compromises
> in one business process to facilitate a single storage solution and
> simplified Administration
> Daemeon (Dæmœn) Reiydelle
> USA 1.415.501.0198
> On Sun, Jun 10, 2018, 02:54 Ralph Soika <ralph.so...@imixs.com> wrote:
>> Hi,
>> I have a general question concerning the Cassandra technology. I already
>> read 2 books but after all I am more and more confused about the question
>> if Cassandra is the right technology. My goal is to store Business Data
>> form a workflow engine into Cassandra. I want to use Cassandra as a kind of
>> archive service because of its fault tolerant and decentralized approach.
>> But here are two things which are confusing me. On the one hand the
>> project claims that a single column value can be 2 GB (1 MB is
>> recommended). On the other hand people explain that a partition should not
>> be larger than 100MB.
>> I plan only one single simple table:
>>     CREATE TABLE documents (
>>        created text,
>>        id text,
>>        data text,
>>        PRIMARY KEY (created,id)
>>     );
>> 'created' is the partition key holding the date in ISO fomat
>> (YYYY-MM-DD). The 'id' is a clustering key and is unique.
>> But my 'data' column holds a XML document with business data. This cell
>> contains many unstructured data and also media data. The data cell will be
>> between 1 and 10 MB. BUT it can also hold more than 100MB and less than 2GB
>> in some cases.
>> Is Cassandra able to handle this kind of table? Or is Cassandra at the
>> end not recommended for this kind of data?
>> For example I would like to ask if data for a specific date is available
>> :
>>     SELECT created,id WHERE created = '2018-06-10'
>> I select without the data column and just ask if data exists. Is the
>> performance automatically poor only because the data cell (no primary key)
>> of some rows is grater then 100MB? Or is cassandra running out of heap
>> space in any case? It is perfectly clear that it makes no sense to select
>> multiple cells which each contain over 100 MB of data in one single query.
>> But this is a fundamental problem and has nothing to do with Cassandra. My
>> java application running in Wildfly would also not be able to handle a data
>> result with multiple GB of data.  But I would expect hat I can select a set
>> of keys just to decide whether to load one single data cell.
>> Cassandra seems like a great system. But many people seem to claim that
>> it is only suitable for mapping a user status list ala Facebook? Is this
>> true? Thanks for you comments in advance.
>> ===
>> Ralph
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