Search Results

Search found 82 results on 4 pages for 'oggz chop'.

Page 4/4 | < Previous Page | 1 2 3 4 

• Of transactions and Mongo

  - by Nuri Halperin

  Originally posted on: http://geekswithblogs.net/nuri/archive/2014/05/20/of-transactions-and-mongo-again.aspxWhat's the first thing you hear about NoSQL databases? That they lose your data? That there's no transactions? No joins? No hope for "real" applications? Well, you *should* be wondering whether a certain of database is the right one for your job. But if you do so, you should be wondering that about "traditional" databases as well! In the spirit of exploration let's take a look at a common challenge: You are a bank. You have customers with accounts. Customer A wants to pay B. You want to allow that only if A can cover the amount being transferred. Let's looks at the problem without any context of any database engine in mind. What would you do? How would you ensure that the amount transfer is done "properly"? Would you prevent a "transaction" from taking place unless A can cover the amount? There are several options: Prevent any change to A's account while the transfer is taking place. That boils down to locking. Apply the change, and allow A's balance to go below zero. Charge person A some interest on the negative balance. Not friendly, but certainly a choice. Don't do either. Options 1 and 2 are difficult to attain in the NoSQL world. Mongo won't save you headaches here either. Option 3 looks a bit harsh. But here's where this can go: ledger. See, and account doesn't need to be represented by a single row in a table of all accounts with only the current balance on it. More often than not, accounting systems use ledgers. And entries in ledgers - as it turns out – don't actually get updated. Once a ledger entry is written, it is not removed or altered. A transaction is represented by an entry in the ledger stating and amount withdrawn from A's account and an entry in the ledger stating an addition of said amount to B's account. For sake of space-saving, that entry in the ledger can happen using one entry. Think {Timestamp, FromAccountId, ToAccountId, Amount}. The implication of the original question – "how do you enforce non-negative balance rule" then boils down to: Insert entry in ledger Run validation of recent entries Insert reverse entry to roll back transaction if validation failed. What is validation? Sum up the transactions that A's account has (all deposits and debits), and ensure the balance is positive. For sake of efficiency, one can roll up transactions and "close the book" on transactions with a pseudo entry stating balance as of midnight or something. This lets you avoid doing math on the fly on too many transactions. You simply run from the latest "approved balance" marker to date. But that's an optimization, and premature optimizations are the root of (some? most?) evil.. Back to some nagging questions though: "But mongo is only eventually consistent!" Well, yes, kind of. It's not actually true that Mongo has not transactions. It would be more descriptive to say that Mongo's transaction scope is a single document in a single collection. A write to a Mongo document happens completely or not at all. So although it is true that you can't update more than one documents "at the same time" under a "transaction" umbrella as an atomic update, it is NOT true that there' is no isolation. So a competition between two concurrent updates is completely coherent and the writes will be serialized. They will not scribble on the same document at the same time. In our case - in choosing a ledger approach - we're not even trying to "update" a document, we're simply adding a document to a collection. So there goes the "no transaction" issue. Now let's turn our attention to consistency. What you should know about mongo is that at any given moment, only on member of a replica set is writable. This means that the writable instance in a set of replicated instances always has "the truth". There could be a replication lag such that a reader going to one of the replicas still sees "old" state of a collection or document. But in our ledger case, things fall nicely into place: Run your validation against the writable instance. It is guaranteed to have a ledger either with (after) or without (before) the ledger entry got written. No funky states. Again, the ledger writing *adds* a document, so there's no inconsistent document state to be had either way. Next, we might worry about data loss. Here, mongo offers several write-concerns. Write-concern in Mongo is a mode that marshals how uptight you want the db engine to be about actually persisting a document write to disk before it reports to the application that it is "done". The most volatile, is to say you don't care. In that case, mongo would just accept your write command and say back "thanks" with no guarantee of persistence. If the server loses power at the wrong moment, it may have said "ok" but actually no written the data to disk. That's kind of bad. Don't do that with data you care about. It may be good for votes on a pole regarding how cute a furry animal is, but not so good for business. There are several other write-concerns varying from flushing the write to the disk of the writable instance, flushing to disk on several members of the replica set, a majority of the replica set or all of the members of a replica set. The former choice is the quickest, as no network coordination is required besides the main writable instance. The others impose extra network and time cost. Depending on your tolerance for latency and read-lag, you will face a choice of what works for you. It's really important to understand that no data loss occurs once a document is flushed to an instance. The record is on disk at that point. From that point on, backup strategies and disaster recovery are your worry, not loss of power to the writable machine. This scenario is not different from a relational database at that point. Where does this leave us? Oh, yes. Eventual consistency. By now, we ensured that the "source of truth" instance has the correct data, persisted and coherent. But because of lag, the app may have gone to the writable instance, performed the update and then gone to a replica and looked at the ledger there before the transaction replicated. Here are 2 options to deal with this. Similar to write concerns, mongo support read preferences. An app may choose to read only from the writable instance. This is not an awesome choice to make for every ready, because it just burdens the one instance, and doesn't make use of the other read-only servers. But this choice can be made on a query by query basis. So for the app that our person A is using, we can have person A issue the transfer command to B, and then if that same app is going to immediately as "are we there yet?" we'll query that same writable instance. But B and anyone else in the world can just chill and read from the read-only instance. They have no basis to expect that the ledger has just been written to. So as far as they know, the transaction hasn't happened until they see it appear later. We can further relax the demand by creating application UI that reacts to a write command with "thank you, we will post it shortly" instead of "thank you, we just did everything and here's the new balance". This is a very powerful thing. UI design for highly scalable systems can't insist that the all databases be locked just to paint an "all done" on screen. People understand. They were trained by many online businesses already that your placing of an order does not mean that your product is already outside your door waiting (yes, I know, large retailers are working on it... but were' not there yet). The second thing we can do, is add some artificial delay to a transaction's visibility on the ledger. The way that works is simply adding some logic such that the query against the ledger never nets a transaction for customers newer than say 15 minutes and who's validation flag is not set. This buys us time 2 ways: Replication can catch up to all instances by then, and validation rules can run and determine if this transaction should be "negated" with a compensating transaction. In case we do need to "roll back" the transaction, the backend system can place the timestamp of the compensating transaction at the exact same time or 1ms after the original one. Effectively, once A or B visits their ledger, both transactions would be visible and the overall balance "as of now" would reflect no change.  The 2 transactions (attempted/ reverted) would be visible , since we do actually account for the attempt. Hold on a second. There's a hole in the story: what if several transfers from A to some accounts are registered, and 2 independent validators attempt to compute the balance concurrently? Is there a chance that both would conclude non-sufficient-funds even though rolling back transaction 100 would free up enough for transaction 117 (some random later transaction)? Yes. there is that chance. But the integrity of the business rule is not compromised, since the prime rule is don't dispense money you don't have. To minimize or eliminate this scenario, we can also assign a single validation process per origin account. This may seem non-scalable, but it can easily be done as a "sharded" distribution. Say we have 11 validation threads (or processing nodes etc.). We divide the account number space such that each validator is exclusively responsible for a certain range of account numbers. Sounds cunningly similar to Mongo's sharding strategy, doesn't it? Each validator then works in isolation. More capacity needed? Chop the account space into more chunks. So where  are we now with the nagging questions? "No joins": Huh? What are those for? "No transactions": You mean no cross-collection and no cross-document transactions? Granted - but don't always need them either. "No hope for real applications": well... There are more issues and edge cases to slog through, I'm sure. But hopefully this gives you some ideas of how to solve common problems without distributed locking and relational databases. But then again, you can choose relational databases if they suit your problem.

  Read the article

• Reworking my singly linked list

  - by Stradigos

  Hello everyone, thanks for taking the time to stop by my question. Below you will find my working SLL, but I want to make more use of C# and, instead of having two classes, SLL and Node, I want to use Node's constructors to do all the work (To where if you pass a string through the node, the constructor will chop it up into char nodes). The problem is, after an a few hours of tinkering, I'm not really getting anywhere... using System; using System.Collections.Generic; using System.Text; using System.IO; namespace PalindromeTester { class Program { static void Main(string[] args) { SLL mySLL = new SLL(); mySLL.add('a'); mySLL.add('b'); mySLL.add('c'); mySLL.add('d'); mySLL.add('e'); mySLL.add('f'); Console.Out.WriteLine("Node count = " + mySLL.count); mySLL.reverse(); mySLL.traverse(); Console.Out.WriteLine("\n The header is: " + mySLL.gethead); Console.In.ReadLine(); } class Node { private char letter; private Node next; public Node() { next = null; } public Node(char c) { this.data = c; } public Node(string s) { } public char data { get { return letter; } set { letter = value; } } public Node nextNode { get { return next; } set { next = value; } } } class SLL { private Node head; private int totalNode; public SLL() { head = null; totalNode = 0; } public void add(char s) { if (head == null) { head = new Node(); head.data = s; } else { Node temp; temp = new Node(); temp.data = s; temp.nextNode = head; head = temp; } totalNode++; } public int count { get { return totalNode; } } public char gethead { get { return head.data; } } public void traverse() { Node temp = head; while(temp != null) { Console.Write(temp.data + " "); temp = temp.nextNode; } } public void reverse() { Node q = null; Node p = this.head; while(p!=null) { Node r=p; p=p.nextNode; r.nextNode=q; q=r; } this.head = q; } } } } Here's what I have so far in trying to work it into Node's constructors: using System; using System.Collections.Generic; using System.Text; using System.IO; namespace PalindromeTester { class Program { static void Main(string[] args) { //Node myList = new Node(); //TextReader tr = new StreamReader("data.txt"); //string line; //while ((line = tr.ReadLine()) != null) //{ // Console.WriteLine(line); //} //tr.Close(); Node myNode = new Node("hello"); Console.Out.WriteLine(myNode.count); myNode.reverse(); myNode.traverse(); // Console.Out.WriteLine(myNode.gethead); Console.In.ReadLine(); } class Node { private char letter; private Node next; private Node head; private int totalNode; public Node() { head = null; totalNode = 0; } public Node(char c) { if (head == null) { head = new Node(); head.data = c; } else { Node temp; temp = new Node(); temp.data = c; temp.nextNode = head; head = temp; } totalNode++; } public Node(string s) { foreach (char x in s) { new Node(x); } } public char data { get { return letter; } set { letter = value; } } public Node nextNode { get { return next; } set { next = value; } } public void reverse() { Node q = null; Node p = this.head; while (p != null) { Node r = p; p = p.nextNode; r.nextNode = q; q = r; } this.head = q; } public void traverse() { Node temp = head; while (temp != null) { Console.Write(temp.data + " "); temp = temp.nextNode; } } public int count { get { return totalNode; } } } } } Ideally, the only constructors and methods I would be left with are Node(), Node(char c), Node(string s), Node reserve() and I'll be reworking traverse into a ToString overload. Any suggestions?

  Read the article

• Converting Encrypted Values

  - by Johnm

  Your database has been protecting sensitive data at rest using the cell-level encryption features of SQL Server for quite sometime. The employees in the auditing department have been inviting you to their after-work gatherings and buying you drinks. Thousands of customers implicitly include you in their prayers of thanks giving as their identities remain safe in your company's database. The cipher text resting snuggly in a column of the varbinary data type is great for security; but it can create some interesting challenges when interacting with other data types such as the XML data type. The XML data type is one that is often used as a message type for the Service Broker feature of SQL Server. It also can be an interesting data type to capture for auditing or integrating with external systems. The challenge that cipher text presents is that the need for decryption remains even after it has experienced its XML metamorphosis. Quite an interesting challenge nonetheless; but fear not. There is a solution. To simulate this scenario, we first will want to create a plain text value for us to encrypt. We will do this by creating a variable to store our plain text value: -- set plain text value DECLARE @PlainText NVARCHAR(255); SET @PlainText = 'This is plain text to encrypt'; The next step will be to create a variable that will store the cipher text that is generated from the encryption process. We will populate this variable by using a pre-defined symmetric key and certificate combination: -- encrypt plain text value DECLARE @CipherText VARBINARY(MAX); OPEN SYMMETRIC KEY SymKey     DECRYPTION BY CERTIFICATE SymCert     WITH PASSWORD='mypassword2010';     SET @CipherText = EncryptByKey                          (                            Key_GUID('SymKey'),                            @PlainText                           ); CLOSE ALL SYMMETRIC KEYS; The value of our newly generated cipher text is 0x006E12933CBFB0469F79ABCC79A583--. This will be important as we reference our cipher text later in this post. Our final step in preparing our scenario is to create a table variable to simulate the existence of a table that contains a column used to hold encrypted values. Once this table variable has been created, populate the table variable with the newly generated cipher text: -- capture value in table variable DECLARE @tbl TABLE (EncVal varbinary(MAX)); INSERT INTO @tbl (EncVal) VALUES (@CipherText); We are now ready to experience the challenge of capturing our encrypted column in an XML data type using the FOR XML clause: -- capture set in xml DECLARE @xml XML; SET @xml = (SELECT               EncVal             FROM @tbl AS MYTABLE             FOR XML AUTO, BINARY BASE64, ROOT('root')); If you add the SELECT @XML statement at the end of this portion of the code you will see the contents of the XML data in its raw format: <root>   <MYTABLE EncVal="AG4Skzy/sEafeavMeaWDBwEAAACE--" /> </root> Strangely, the value that is captured appears nothing like the value that was created through the encryption process. The result being that when this XML is converted into a readable data set the encrypted value will not be able to be decrypted, even with access to the symmetric key and certificate used to perform the decryption. An immediate thought might be to convert the varbinary data type to either a varchar or nvarchar before creating the XML data. This approach makes good sense. The code for this might look something like the following: -- capture set in xml DECLARE @xml XML; SET @xml = (SELECT              CONVERT(NVARCHAR(MAX),EncVal) AS EncVal             FROM @tbl AS MYTABLE             FOR XML AUTO, BINARY BASE64, ROOT('root')); However, this results in the following error: Msg 9420, Level 16, State 1, Line 26 XML parsing: line 1, character 37, illegal xml character A quick query that returns CONVERT(NVARCHAR(MAX),EncVal) reveals that the value that is causing the error looks like something off of a genuine Chinese menu. While this situation does present us with one of those spine-tingling, expletive-generating challenges, rest assured that this approach is on the right track. With the addition of the "style" argument to the CONVERT method, our solution is at hand. When dealing with converting varbinary data types we have three styles available to us: - The first is to not include the style parameter, or use the value of "0". As we see, this style will not work for us. - The second option is to use the value of "1" will keep our varbinary value including the "0x" prefix. In our case, the value will be 0x006E12933CBFB0469F79ABCC79A583-- - The third option is to use the value of "2" which will chop the "0x" prefix off of our varbinary value. In our case, the value will be 006E12933CBFB0469F79ABCC79A583-- Since we will want to convert this back to varbinary when reading this value from the XML data we will want the "0x" prefix, so we will want to change our code as follows: -- capture set in xml DECLARE @xml XML; SET @xml = (SELECT              CONVERT(NVARCHAR(MAX),EncVal,1) AS EncVal             FROM @tbl AS MYTABLE             FOR XML AUTO, BINARY BASE64, ROOT('root')); Once again, with the inclusion of the SELECT @XML statement at the end of this portion of the code you will see the contents of the XML data in its raw format: <root>   <MYTABLE EncVal="0x006E12933CBFB0469F79ABCC79A583--" /> </root> Nice! We are now cooking with gas. To continue our scenario, we will want to parse the XML data into a data set so that we can glean our freshly captured cipher text. Once we have our cipher text snagged we will capture it into a variable so that it can be used during decryption: -- read back xml DECLARE @hdoc INT; DECLARE @EncVal NVARCHAR(MAX); EXEC sp_xml_preparedocument @hDoc OUTPUT, @xml; SELECT @EncVal = EncVal FROM OPENXML (@hdoc, '/root/MYTABLE') WITH ([EncVal] VARBINARY(MAX) '@EncVal'); EXEC sp_xml_removedocument @hDoc; Finally, the decryption of our cipher text using the DECRYPTBYKEYAUTOCERT method and the certificate utilized to perform the encryption earlier in our exercise: SELECT     CONVERT(NVARCHAR(MAX),                     DecryptByKeyAutoCert                          (                            CERT_ID('AuditLogCert'),                            N'mypassword2010',                            @EncVal                           )                     ) EncVal; Ah yes, another hurdle presents itself! The decryption produced the value of NULL which in cryptography means that either you don't have permissions to decrypt the cipher text or something went wrong during the decryption process (ok, sometimes the value is actually NULL; but not in this case). As we see, the @EncVal variable is an nvarchar data type. The third parameter of the DECRYPTBYKEYAUTOCERT method requires a varbinary value. Therefore we will need to utilize our handy-dandy CONVERT method: SELECT     CONVERT(NVARCHAR(MAX),                     DecryptByKeyAutoCert                          (                             CERT_ID('AuditLogCert'),                             N'mypassword2010',                             CONVERT(VARBINARY(MAX),@EncVal)                           )                     ) EncVal; Oh, almost. The result remains NULL despite our conversion to the varbinary data type. This is due to the creation of an varbinary value that does not reflect the actual value of our @EncVal variable; but rather a varbinary conversion of the variable itself. In this case, something like 0x3000780030003000360045003--. Considering the "style" parameter got us past XML challenge, we will want to consider its power for this challenge as well. Knowing that the value of "1" will provide us with the actual value including the "0x", we will opt to utilize that value in this case: SELECT     CONVERT(NVARCHAR(MAX),                     DecryptByKeyAutoCert                          (                            CERT_ID('SymCert'),                            N'mypassword2010',                            CONVERT(VARBINARY(MAX),@EncVal,1)                           )                     ) EncVal; Bingo, we have success! We have discovered what happens with varbinary data when captured as XML data. We have figured out how to make this data useful post-XML-ification. Best of all we now have a choice in after-work parties now that our very happy client who depends on our XML based interface invites us for dinner in celebration. All thanks to the effective use of the style parameter.

  Read the article

• How to Load Oracle Tables From Hadoop Tutorial (Part 5 - Leveraging Parallelism in OSCH)

  - by Bob Hanckel

  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 Using OSCH: Beyond Hello World In the previous post we discussed a “Hello World” example for OSCH focusing on the mechanics of getting a toy end-to-end example working. In this post we are going to talk about how to make it work for big data loads. We will explain how to optimize an OSCH external table for load, paying particular attention to Oracle’s DOP (degree of parallelism), the number of external table location files we use, and the number of HDFS files that make up the payload. We will provide some rules that serve as best practices when using OSCH. The assumption is that you have read the previous post and have some end to end OSCH external tables working and now you want to ramp up the size of the loads. Using OSCH External Tables for Access and Loading OSCH external tables are no different from any other Oracle external tables.  They can be used to access HDFS content using Oracle SQL: SELECT * FROM my_hdfs_external_table; or use the same SQL access to load a table in Oracle. INSERT INTO my_oracle_table SELECT * FROM my_hdfs_external_table; To speed up the load time, you will want to control the degree of parallelism (i.e. DOP) and add two SQL hints. ALTER SESSION FORCE PARALLEL DML PARALLEL  8; ALTER SESSION FORCE PARALLEL QUERY PARALLEL 8; INSERT /*+ append pq_distribute(my_oracle_table, none) */ INTO my_oracle_table SELECT * FROM my_hdfs_external_table; There are various ways of either hinting at what level of DOP you want to use.  The ALTER SESSION statements above force the issue assuming you (the user of the session) are allowed to assert the DOP (more on that in the next section).  Alternatively you could embed additional parallel hints directly into the INSERT and SELECT clause respectively. /*+ parallel(my_oracle_table,8) *//*+ parallel(my_hdfs_external_table,8) */ Note that the "append" hint lets you load a target table by reserving space above a given "high watermark" in storage and uses Direct Path load.  In other doesn't try to fill blocks that are already allocated and partially filled. It uses unallocated blocks.  It is an optimized way of loading a table without incurring the typical resource overhead associated with run-of-the-mill inserts.  The "pq_distribute" hint in this context unifies the INSERT and SELECT operators to make data flow during a load more efficient. Finally your target Oracle table should be defined with "NOLOGGING" and "PARALLEL" attributes.   The combination of the "NOLOGGING" and use of the "append" hint disables REDO logging, and its overhead.  The "PARALLEL" clause tells Oracle to try to use parallel execution when operating on the target table. Determine Your DOP It might feel natural to build your datasets in Hadoop, then afterwards figure out how to tune the OSCH external table definition, but you should start backwards. You should focus on Oracle database, specifically the DOP you want to use when loading (or accessing) HDFS content using external tables. The DOP in Oracle controls how many PQ slaves are launched in parallel when executing an external table. Typically the DOP is something you want to Oracle to control transparently, but for loading content from Hadoop with OSCH, it's something that you will want to control. Oracle computes the maximum DOP that can be used by an Oracle user. The maximum value that can be assigned is an integer value typically equal to the number of CPUs on your Oracle instances, times the number of cores per CPU, times the number of Oracle instances. For example, suppose you have a RAC environment with 2 Oracle instances. And suppose that each system has 2 CPUs with 32 cores. The maximum DOP would be 128 (i.e. 2*2*32). In point of fact if you are running on a production system, the maximum DOP you are allowed to use will be restricted by the Oracle DBA. This is because using a system maximum DOP can subsume all system resources on Oracle and starve anything else that is executing. Obviously on a production system where resources need to be shared 24x7, this can’t be allowed to happen. The use cases for being able to run OSCH with a maximum DOP are when you have exclusive access to all the resources on an Oracle system. This can be in situations when your are first seeding tables in a new Oracle database, or there is a time where normal activity in the production database can be safely taken off-line for a few hours to free up resources for a big incremental load. Using OSCH on high end machines (specifically Oracle Exadata and Oracle BDA cabled with Infiniband), this mode of operation can load up to 15TB per hour. The bottom line is that you should first figure out what DOP you will be allowed to run with by talking to the DBAs who manage the production system. You then use that number to derive the number of location files, and (optionally) the number of HDFS data files that you want to generate, assuming that is flexible. Rule 1: Find out the maximum DOP you will be allowed to use with OSCH on the target Oracle system Determining the Number of Location Files Let’s assume that the DBA told you that your maximum DOP was 8. You want the number of location files in your external table to be big enough to utilize all 8 PQ slaves, and you want them to represent equally balanced workloads. Remember location files in OSCH are metadata lists of HDFS files and are created using OSCH’s External Table tool. They also represent the workload size given to an individual Oracle PQ slave (i.e. a PQ slave is given one location file to process at a time, and only it will process the contents of the location file.) Rule 2: The size of the workload of a single location file (and the PQ slave that processes it) is the sum of the content size of the HDFS files it lists For example, if a location file lists 5 HDFS files which are each 100GB in size, the workload size for that location file is 500GB. The number of location files that you generate is something you control by providing a number as input to OSCH’s External Table tool. Rule 3: The number of location files chosen should be a small multiple of the DOP Each location file represents one workload for one PQ slave. So the goal is to keep all slaves busy and try to give them equivalent workloads. Obviously if you run with a DOP of 8 but have 5 location files, only five PQ slaves will have something to do and the other three will have nothing to do and will quietly exit. If you run with 9 location files, then the PQ slaves will pick up the first 8 location files, and assuming they have equal work loads, will finish up about the same time. But the first PQ slave to finish its job will then be rescheduled to process the ninth location file, potentially doubling the end to end processing time. So for this DOP using 8, 16, or 32 location files would be a good idea. Determining the Number of HDFS Files Let’s start with the next rule and then explain it: Rule 4: The number of HDFS files should try to be a multiple of the number of location files and try to be relatively the same size In our running example, the DOP is 8. This means that the number of location files should be a small multiple of 8. Remember that each location file represents a list of unique HDFS files to load, and that the sum of the files listed in each location file is a workload for one Oracle PQ slave. The OSCH External Table tool will look in an HDFS directory for a set of HDFS files to load.  It will generate N number of location files (where N is the value you gave to the tool). It will then try to divvy up the HDFS files and do its best to make sure the workload across location files is as balanced as possible. (The tool uses a greedy algorithm that grabs the biggest HDFS file and delegates it to a particular location file. It then looks for the next biggest file and puts in some other location file, and so on). The tools ability to balance is reduced if HDFS file sizes are grossly out of balance or are too few. For example suppose my DOP is 8 and the number of location files is 8. Suppose I have only 8 HDFS files, where one file is 900GB and the others are 100GB. When the tool tries to balance the load it will be forced to put the singleton 900GB into one location file, and put each of the 100GB files in the 7 remaining location files. The load balance skew is 9 to 1. One PQ slave will be working overtime, while the slacker PQ slaves are off enjoying happy hour. If however the total payload (1600 GB) were broken up into smaller HDFS files, the OSCH External Table tool would have an easier time generating a list where each workload for each location file is relatively the same.  Applying Rule 4 above to our DOP of 8, we could divide the workload into160 files that were approximately 10 GB in size.  For this scenario the OSCH External Table tool would populate each location file with 20 HDFS file references, and all location files would have similar workloads (approximately 200GB per location file.) As a rule, when the OSCH External Table tool has to deal with more and smaller files it will be able to create more balanced loads. How small should HDFS files get? Not so small that the HDFS open and close file overhead starts having a substantial impact. For our performance test system (Exadata/BDA with Infiniband), I compared three OSCH loads of 1 TiB. One load had 128 HDFS files living in 64 location files where each HDFS file was about 8GB. I then did the same load with 12800 files where each HDFS file was about 80MB size. The end to end load time was virtually the same. However when I got ridiculously small (i.e. 128000 files at about 8MB per file), it started to make an impact and slow down the load time. What happens if you break rules 3 or 4 above? Nothing draconian, everything will still function. You just won’t be taking full advantage of the generous DOP that was allocated to you by your friendly DBA. The key point of the rules articulated above is this: if you know that HDFS content is ultimately going to be loaded into Oracle using OSCH, it makes sense to chop them up into the right number of files roughly the same size, derived from the DOP that you expect to use for loading. Next Steps So far we have talked about OLH and OSCH as alternative models for loading. That’s not quite the whole story. They can be used together in a way that provides for more efficient OSCH loads and allows one to be more flexible about scheduling on a Hadoop cluster and an Oracle Database to perform load operations. The next lesson will talk about Oracle Data Pump files generated by OLH, and loaded using OSCH. It will also outline the pros and cons of using various load methods.  This will be followed up with a final tutorial lesson focusing on how to optimize OLH and OSCH for use on Oracle's engineered systems: specifically Exadata and the BDA. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}

  Read the article

• Will these optimizations to my Ruby implementation of diff improve performance in a Rails app?

  - by grg-n-sox

  <tl;dr> In source version control diff patch generation, would it be worth it to use the optimizations listed at the very bottom of this writing (see <optimizations>) in my Ruby implementation of diff for making diff patches? </tl;dr> <introduction> I am programming something I have never done before and there might already be tools out there to do the exact thing I am programming but at this point I am having too much fun to care so I am still going to do it from scratch, even if there is a tool for this. So anyways, I am working on a Ruby on Rails app and need a certain feature. Basically I want each entry in a table of mine, let's say for example a table of video games, to have a stored chunk of text that represents a review or something of the sort for that table entry. However, I want this text to be both editable by any registered user and also keep track of different submissions in a version control system. The simplest solution I could think of is just implement a solution that keeps track of the text body and the diff patch history of different versions of the text body as objects in Ruby and then serialize it, preferably in human readable form (so I'll most likely use YAML for this) for editing if needed due to corruption by a software bug or a mistake is made by an admin doing some version editing. So at first I just tried to dive in head first into this feature to find that the problem of generating a diff patch is more difficult that I thought to do efficiently. So I did some research and came across some ideas. Some I have implemented already and some I have not. However, it all pretty much revolves around the longest common subsequence problem, as you would already know if you have already done anything with diff or diff-like features, and optimization the function that solves it. Currently I have it so it truncates the compared versions of the text body from the beginning and end until non-matching lines are found. Then it solves the problem using a comparison matrix, but instead of incrementing the value stored in a cell when it finds a matching line like in most longest common subsequence algorithms I have seen examples of, I increment when I have a non-matching line so as to calculate edit distance instead of longest common subsequence. Although as far as I can tell between the two approaches, they are essentially two sides of the same coin so either could be used to derive an answer. It then back-traces through the comparison matrix and notes when there was an incrementation and in which adjacent cell (West, Northwest, or North) to determine that line's diff entry and assumes all other lines to be unchanged. Normally I would leave it at that, but since this is going into a Rails environment and not just some stand-alone Ruby script, I started getting worried about needing to optimize at least enough so if a spammer that somehow knew how I implemented the version control system and knew my worst case scenario entry still wouldn't be able to hit the server that bad. After some searching and reading of research papers and articles through the internet, I've come across several that seem decent but all seem to have pros and cons and I am having a hard time deciding how well in this situation that the pros and cons balance out. So are the ones listed here worth it? I have listed them with known pros and cons. </introduction> <optimizations> Chop the compared sequences into multiple chucks of subsequences by splitting where lines are unchanged, and then truncating each section of unchanged lines at the beginning and end of each section. Then solve the edit distance of each subsequence. Pro: Changes the time increase as the changed area gets bigger from a quadratic increase to something more similar to a linear increase. Con: Figuring out where to split already seems like you have to solve edit distance except now you don't care how it is changed. Would be fine if this was solvable by a process closer to solving hamming distance but a single insertion would throw this off. Use a cryptographic hash function to both convert all sequence elements into integers and ensure uniqueness. Then solve the edit distance comparing the hash integers instead of the sequence elements themselves. Pro: The operation of comparing two integers is faster than the operation of comparing two strings, so a slight performance gain is received after every comparison, which can be a lot overall. Con: Using a cryptographic hash function takes time to convert all the sequence elements and may end up costing more time to do the conversion that you gain back from the integer comparisons. You could use the built in hash function for a string but that will not guarantee uniqueness. Use lazy evaluation to only calculate the three center-most diagonals of the comparison matrix and then only calculate additional diagonals as needed. And then also use this approach to possibly remove the need on some comparisons to compare all three adjacent cells as desribed here. Pro: Can turn an algorithm that always takes O(n * m) time and make it so only worst case scenario is that time, best case becomes practically linear, and average case is somewhere between the two. Con: It is an algorithm I've only seen implemented in functional programming languages and I am having a difficult time comprehending how to convert this into Ruby based on how it is described at the site linked to above. Make a C module and do the hard work at the native level in C and just make a Ruby wrapper for it so Ruby can make all the calls to it that it needs. Pro: I have to imagine that evaluating something like this in could be a LOT faster. Con: I have no idea how Rails handles apps with ruby code that has C extensions and it hurts the portability of the app. This is an optimization for after the solving of edit distance, but idea is to store additional combined diffs with the ones produced by each version to make a delta-tree data structure with the most recently made diff as the root node of the tree so getting to any version takes worst case time of O(log n) instead of O(n). Pro: Would make going back to an old version a lot faster. Con: It would mean every new commit, the delta-tree would get a new root node that will cost time to reorganize the delta-tree for an operation that will be carried out a lot more often than going back a version, not to mention the unlikelihood it will be an old version. </optimizations> So are these things worth the effort?

  Read the article

• Decompiling a *.DLL to assembly for .net in delphi 4

  - by Lex Dean

  I love my Delphi 4 but at the same time I see the need to talk to windows .net This is a recompiled dll that I found on sourceforge.net/projects/delphinet/ (DelphiNet03.zip) by some nice people that fund the dll from some were. The real answer is to make this dll so that fits into Delphi as true Delphi code, and not a dll clip on. So we can make objects that use dot net in Delphi. Because I’m not an assembly freak, I’m freaking out a little with a wee sweek for help! 1/ How do I link the asm code with the data info at the bottom of this code. Can some one show me which calls to look for to make this link to data. 2/ I need to find the beginning of all the procedures and functions, but I cannot find a ‘RET’ statement. And what line is the beginning statement in this code. 3/ How do I identify were the jump statements go to, put them into Delphi format In this code it looks I can do:- jle 402890h \1000:00402854 7e3a add [eax], al \1000:00402856 0000 …………………………………………….. or ch, [edi+3eh] \1000:0040288d 0a6f3e xrefs first: 1000:00402854 number : 1 \; add [eax], al \1000:00402890 0000 //******************************* jle @@21 \\1000:00402854 7e3a add [eax], al \\1000:00402856 0000 …………………………………………….. or ch, [edi+3eh] \1000:0040288d 0a6f3e xrefs first: 1000:00402854 number : 1 \; @@21 add [eax], al \1000:00402890 0000 Is that a correct conversion. I think a xrefs first: 1000:004021d1 number : 1 is the best to follow 4/ I need a good reference on 8086 up assembly code that I can print out and get to learn properly. I found this asm decomplier of http://www.cronos.cc/ that is so similar to Delphi that it only needs a little more convertion to get it into Delphi asm I think. It’s only taken me 3 hours to get the file into TMemo and to write a few lines to chop the line over in a stream and reload the memo. Help please Email: [email protected] xrefs first: 1000:004041ae number : 1 \\; dd 4190h \\1000:00402000 90410000 dd 00h \\1000:00402004 00000000 dec eax \\1000:00402008 48 add [eax], al \\1000:00402009 0000 add [edx], al \\1000:0040200b 0002 add [eax], al \\1000:0040200d 0000 add [eax-2bffffd2h], al \\1000:0040200f 00802e0000d4 adc al, [eax] \\1000:00402015 1200 add [ecx], al \\1000:00402017 0001 add [eax], al \\1000:00402019 0000 add [eax], al \\1000:0040201b 0000 add [eax], al \\1000:0040201d 0000 add [eax], al \\1000:0040201f 0000 add [eax], al \\1000:00402021 0000 add [eax], al \\1000:00402023 0000 add [eax], al \\1000:00402025 0000 add [eax], al \\1000:00402027 0000 add [eax], al \\1000:00402029 0000 add [eax], al \\1000:0040202b 0000 add [eax], al \\1000:0040202d 0000 add [eax], al \\1000:0040202f 0000 add [eax], al \\1000:00402031 0000 add [eax], al \\1000:00402033 0000 add [eax], al \\1000:00402035 0000 add [eax], al \\1000:00402037 0000 add [eax], al \\1000:00402039 0000 add [eax], al \\1000:0040203b 0000 add [eax], al \\1000:0040203d 0000 add [eax], al \\1000:0040203f 0000 add [eax], al \\1000:00402041 0000 add [eax], al \\1000:00402043 0000 add [eax], al \\1000:00402045 0000 add [eax], al \\1000:00402047 0000 add [eax], al \\1000:00402049 0000 add [eax], al \\1000:0040204b 0000 add [eax], al \\1000:0040204d 0000 add [ebx], dl \\1000:0040204f 0013 xor [eax+eax], al \\1000:00402051 300400 or al, [ecx] \\1000:00402054 0a01 add [eax], al \\1000:00402056 0000 add [eax], eax \\1000:00402058 0100 add [ecx], dl \\1000:0040205a 0011 push cs \\1000:0040205c 0e add al, 50h \\1000:0040205d 0450 mov gs, [ecx+05h] \\1000:0040205f 8e6905 push eax \\1000:00402062 50 mov gs, [ecx+2eh] \\1000:00402063 8e692e add eax, f938h \\1000:00402066 0538f90000 add [ebx], al \\1000:0040206b 0003 jc 402070h \\1000:0040206d 7201 add [eax], al \\1000:0040206f 0000 jo 40209bh \\1000:00402071 7028 add al, [eax] \\1000:00402073 0200 add [edx], cl \\1000:00402075 000a sub eax, 36f0408h \\1000:00402077 2d08046f03 add [eax], al \\1000:0040207c 0000 or ch, [ebx] \\1000:0040207e 0a2b push es \\1000:00402080 06 add al, 6fh \\1000:00402081 046f add al, 00h \\1000:00402083 0400 add [edx], cl \\1000:00402085 000a adc eax, [edi] \\1000:00402087 1307 push ss \\1000:00402089 16 adc ecx, [eax] \\1000:0040208a 1308 cmp cl, cl \\1000:0040208c 38c9 add [eax], al \\1000:0040208e 0000 add [ecx], dl \\1000:00402090 0011 pop es \\1000:00402092 07 adc [eax], ecx \\1000:00402093 1108 callf 056f:060a9a08h \\1000:00402095 9a0a066f05 add [eax], al \\1000:0040209a 0000 or cl, [ebx] \\1000:0040209c 0a0b push es \\1000:0040209e 06 outsd \\1000:0040209f 6f push es \\1000:004020a0 06 add [eax], al \\1000:004020a1 0000 or al, [ebx] \\1000:004020a3 0a03 sub [edx], al \\1000:004020a5 2802 add [eax], al \\1000:004020a7 0000 or bh, [ecx] \\1000:004020a9 0a39 movsd \\1000:004020ab a5 add [eax], al \\1000:004020ac 0000 add [edi], al \\1000:004020ae 0007 mov gs, [ecx+0eh] \\1000:004020b0 8e690e add al, 50h \\1000:004020b3 0450 mov gs, [ecx+40h] \\1000:004020b5 8e6940 cwde \\1000:004020b8 98 add [eax], al \\1000:004020b9 0000 add [edi], dl \\1000:004020bb 0017 or al, 16h \\1000:004020bd 0c16 or eax, 9072b2bh \\1000:004020bf 0d2b2b0709 callf 0000:076f9a09h \\1000:004020c4 9a6f070000 or ch, [edi+08h] \\1000:004020c9 0a6f08 add [eax], al \\1000:004020cc 0000 or ch, [eax+ebx] \\1000:004020ce 0a2c18 push cs \\1000:004020d1 0e add al, 50h \\1000:004020d2 0450 or [edx+d72h], ebx \\1000:004020d4 099a720d0000 jo 402104h \\1000:004020da 7028 or [eax], eax \\1000:004020dc 0900 add [edx], cl \\1000:004020de 000a add dl, cs:[esi] \\1000:004020e0 2e0216 or al, 08h \\1000:004020e3 0c08 sub eax, 90c2b02h \\1000:004020e5 2d022b0c09 pop ss \\1000:004020ea 17 pop eax \\1000:004020eb 58 or eax, 50040e09h \\1000:004020ec 0d090e0450 mov gs, [ecx+32h] \\1000:004020f1 8e6932 int 08h \\1000:004020f4 cd08 sub al, 5ch \\1000:004020f6 2c5c push ss \\1000:004020f8 16 adc eax, [ebx+ebp] \\1000:004020f9 13042b dec esi \\1000:004020fc 4e push cs \\1000:004020fd 0e add al, 50h \\1000:004020fe 0450 adc [edx+ebx*4], eax \\1000:00402100 11049a jc 402112h \\1000:00402103 720d add [eax], al \\1000:00402105 0000 jo 402131h \\1000:00402107 7028 or [eax], eax \\1000:00402109 0900 add [edx], cl \\1000:0040210b 000a xor esi, [esi] \\1000:0040210d 3336 pop es \\1000:0040210f 07 adc [edx+ebx*4], eax \\1000:00402110 11049a outsd \\1000:00402113 6f pop es \\1000:00402114 07 add [eax], al \\1000:00402115 0000 or ch, [edi+0ah] \\1000:00402117 0a6f0a add [eax], al \\1000:0040211a 0000 or dl, [ebx] \\1000:0040211c 0a13 push es \\1000:0040211e 06 add eax, 9a041150h \\1000:0040211f 055011049a sub [ebx], cl \\1000:00402124 280b add [eax], al \\1000:00402126 0000 or dl, [edx] \\1000:00402128 0a12 push es \\1000:0040212a 06 adc al, [c28h] \\1000:0040212b 1205280c0000 xrefs first: 1000:00402107 number : 1 \\; or ch, [edx+eax] \\1000:00402131 0a2c02 sub ebx, [esi] \\1000:00402134 2b1e push cs \\1000:00402136 0e add al, 50h \\1000:00402137 0450 adc [edi+eax], eax \\1000:00402139 110407 adc [edx+ebx*4], eax \\1000:0040213c 11049a outsd \\1000:0040213f 6f pop es \\1000:00402140 07 add [eax], al \\1000:00402141 0000 or ah, [edx+58170411h] \\1000:00402143 0aa211041758 adc eax, [ecx+edx] \\1000:00402149 130411 add al, 0eh \\1000:0040214c 040e add al, 50h \\1000:0040214e 0450 mov gs, [ecx+32h] \\1000:00402150 8e6932 test eax, 58170811h \\1000:00402153 a911081758 adc ecx, [eax] \\1000:00402158 1308 adc [eax], ecx \\1000:0040215a 1108 adc [edi], eax \\1000:0040215c 1107 mov gs, [ecx+3fh] \\1000:0040215e 8e693f sub al, ffh \\1000:00402161 2cff db ff \\1000:00402163 ff jmp [edx] \\1000:00402164 ff2a add [eax], al \\1000:00402166 0000 adc esi, [eax] \\1000:00402168 1330 add eax, 8100h \\1000:0040216a 0500810000 add [edx], al \\1000:0040216f 0002 add [eax], al \\1000:00402171 0000 adc [edx+esi*2], eax \\1000:00402173 110472 xor eax, [eax] \\1000:00402176 3300 add [eax+28h], dh \\1000:00402178 007028 add al, [eax] \\1000:0040217b 0200 add [edx], cl \\1000:0040217d 000a sub al, 09h \\1000:0040217f 2c09 add ebp, [eax] \\1000:00402181 0328 or eax, a0a0000h \\1000:00402183 0d00000a0a sub eax, [edi] \\1000:00402188 2b07 add al, 28h \\1000:0040218a 0428 push cs \\1000:0040218c 0e add [eax], al \\1000:0040218d 0000 or cl, [edx] \\1000:0040218f 0a0a push es \\1000:00402191 06 add eax, f6f1717h \\1000:00402192 0517176f0f add [eax], al \\1000:00402197 0000 or cl, [ebx] \\1000:00402199 0a0b push ss \\1000:0040219b 16 lea eax, [edx] \\1000:0040219c 8d02 add [eax], al \\1000:0040219e 0000 add [esi+ecx], ecx \\1000:004021a0 010c0e add al, 2ch \\1000:004021a3 042c push cs \\1000:004021a5 260e add al, 8eh \\1000:004021a7 048e c160d imul edi, [eax+28dh], d160c01h \\1000:004021a9 69b88d020000010c sub edx, [eax] \\1000:004021b3 2b10 or [ecx], cl \\1000:004021b5 0809 push cs \\1000:004021b7 0e add al, 09h \\1000:004021b8 0409 callf 0000:106f9a09h \\1000:004021ba 9a6f100000 or ah, [edx+d581709h] \\1000:004021bf 0aa20917580d or [esi], ecx \\1000:004021c5 090e add al, 8eh \\1000:004021c7 048e imul esi, [edx], 17202e9h \\1000:004021c9 6932e9027201 add [eax], al \\1000:004021cf 0000 jo 4021dah \\1000:004021d1 7007 db 0f \\1000:004021d3 0f add al, 12h \\1000:004021d4 0412 add ch, [eax] \\1000:004021d6 0228 add [eax], eax \\1000:004021d8 0100 xrefs first: 1000:004021d1 number : 1 \\; add [esi], al \\1000:004021da 0006 pop es \\1000:004021dc 07 or [edi+11h], ch \\1000:004021dd 086f11 add [eax], al \\1000:004021e0 0000 or dl, [ebx] \\1000:004021e2 0a13 add al, 11h \\1000:004021e4 0411 add al, 0eh \\1000:004021e6 040e add al, 6fh \\1000:004021e8 046f adc al, [eax] \\1000:004021ea 1200 add [edx], cl \\1000:004021ec 000a adc eax, [511002bh] \\1000:004021ee 13052b001105 sub al, [eax] \\1000:004021f4 2a00 add [eax], al \\1000:004021f6 0000 adc esi, [eax] \\1000:004021f8 1330 add eax, 4e00h \\1000:004021fa 05004e0000 add [ebx], al \\1000:004021ff 0003 add [eax], al \\1000:00402201 0000 adc [ebx], eax \\1000:00402203 1103 outsd \\1000:00402205 6f adc [eax], al \\1000:00402206 1000 add [edx], cl \\1000:00402208 000a or al, [8db8698eh] \\1000:0040220a 0a058e69b88d add al, [eax] \\1000:00402210 0200 add [ecx], al \\1000:00402212 0001 or edx, [esi] \\1000:00402214 0b16 or al, 2bh \\1000:00402216 0c2b db 0f \\1000:00402218 0f pop es \\1000:00402219 07 or [106f9a08h], al \\1000:0040221a 0805089a6f10 add [eax], al \\1000:00402220 0000 or ah, [edx+c581708h] \\1000:00402222 0aa20817580c or [eb32698eh], al \\1000:00402228 08058e6932eb add al, [esi+eax] \\1000:0040222e 020406 lsl edx, [edx] \\1000:00402231 0f0312 add [eax], ebp \\1000:00402234 0128 add [eax], eax \\1000:00402236 0100 add [esi], al \\1000:00402238 0006 push es \\1000:0040223a 06 add al, 07h \\1000:0040223b 0407 outsd \\1000:0040223d 6f adc eax, [eax] \\1000:0040223e 1300 add [edx], cl \\1000:00402240 000a or eax, 6f050309h \\1000:00402242 0d0903056f adc al, 00h \\1000:00402247 1400 add [edx], cl \\1000:00402249 000a adc eax, [ebx+ebp] \\1000:0040224b 13042b add [ecx], dl \\1000:0040224e 0011 add al, 2ah \\1000:00402250 042a add [eax], al \\1000:00402252 0000 adc esi, [eax] \\1000:00402254 1330 add eax, 7600h \\1000:00402256 0500760000 add [eax+eax], al \\1000:0040225b 000400 add [ecx], dl \\1000:0040225e 0011 add al, 72h \\1000:00402260 0472 xor eax, [eax] \\1000:00402262 3300 add [eax+28h], dh \\1000:00402264 007028 add al, [eax] \\1000:00402267 0200 add [edx], cl \\1000:00402269 000a sub al, 09h \\1000:0040226b 2c09 add ebp, [eax] \\1000:0040226d 0328 or eax, a0a0000h \\1000:0040226f 0d00000a0a sub eax, [edi] \\1000:00402274 2b07 add al, 28h \\1000:00402276 0428 push cs \\1000:00402278 0e add [eax], al \\1000:00402279 0000 or cl, [edx] \\1000:0040227b 0a0a push es \\1000:0040227d 06 add eax, f6f1717h \\1000:0040227e 0517176f0f add [eax], al \\1000:00402283 0000 or cl, [ebx] \\1000:00402285 0a0b push cs \\1000:00402287 0e add eax, 8db8698eh \\1000:00402288 058e69b88d add al, [eax] \\1000:0040228d 0200 add [ecx], al \\1000:0040228f 0001 or al, 16h \\1000:00402291 0c16 or eax, 908102bh \\1000:00402293 0d2b100809 push cs \\1000:00402298 0e add eax, 106f9a09h \\1000:00402299 05099a6f10 add [eax], al \\1000:0040229e 0000 or ah, [edx+d581709h] \\1000:004022a0 0aa20917580d or [esi], ecx \\1000:004022a6 090e add eax, e932698eh \\1000:004022a8 058e6932e9 add cl, [esi] \\1000:004022ad 020e add al, 07h \\1000:004022af 0407 db 0f \\1000:004022b1 0f add eax, 1280212h \\1000:004022b2 0512022801 add [eax], al \\1000:004022b7 0000 push es \\1000:004022b9 06 pop es \\1000:004022ba 07 push cs \\1000:004022bb 0e add al, 08h \\1000:004022bc 0408 outsd \\1000:004022be 6f adc eax, [eax] \\1000:004022bf 1300 add [edx], cl \\1000:004022c1 000a adc eax, [ecx+edx] \\1000:004022c3 130411 add al, 14h \\1000:004022c6 0414 push cs \\1000:004022c8 0e add eax, 146fh \\1000:004022c9 056f140000 or dl, [ebx] \\1000:004022ce 0a13 add eax, 511002bh \\1000:004022d0 052b001105 sub al, [eax] \\1000:004022d5 2a00 add [ebx], dl \\1000:004022d7 0013 xor [eax+eax], al \\1000:004022d9 300400 jbe 4022deh \\1000:004022dc 7600 xrefs first: 1000:004022dc number : 1 \\; add fs:[esi+45h], cl \\1000:004034fc 64004e45 push esp \\1000:00403500 54 dec ecx \\1000:00403501 49 xrefs first: 1000:004034b2 number : 1 \\; outsb \\1000:00403502 6e jbe 403574h \\1000:00403503 766f imul esp, [ebp+43h], 6ch \\1000:00403505 6b65436c popad \\1000:00403509 61 jnc 40357fh \\1000:0040350a 7373 dec ebp \\1000:0040350c 4d jz 403578h \\1000:0040350d 657468 outsd \\1000:00403510 6f add fs:[esi+45h], cl \\1000:00403511 64004e45 push esp \\1000:00403515 54 push ebx \\1000:00403516 53 jz 40355fh \\1000:00403517 657445 outsb \\1000:0040351a 6e jnz 40358ah \\1000:0040351b 756d push esi \\1000:0040351d 56 xrefs first: 1000:004034b7 number : 1 \\; popad \\1000:0040351e 61 insb \\1000:0040351f 6c jnz 403587h \\1000:00403520 7565 add [esi+45h], cl \\1000:00403522 004e45 push esp \\1000:00403525 54 inc edi \\1000:00403526 47 db 65 ;'e' \\1000:00403527 65 xrefs first: 1000:004034be number : 1 \\; db 74 ;'t' \\1000:00403528 74 db 50 ;'p' \\1000:00403529 50 db 72 ;'r' \\1000:0040352a 72 db 6f ;'o' \\1000:0040352b 6f db 70 ;'p' \\1000:0040352c 70 db 65 ;'e' \\1000:0040352d 65 db 72 ;'r' \\1000:0040352e 72 db 74 ;'t' \\1000:0040352f 74 db 79 ;'y' \\1000:00403530 79 db 00 \\1000:00403531 00 db 4e ;'n' \\1000:00403532 4e db 45 ;'e' \\1000:00403533 45 db 54 ;'t' \\1000:00403534 54 db 47 ;'g' \\1000:00403535 47 db 65 ;'e' \\1000:00403536 65 db 74 ;'t' \\1000:00403537 74 db 46 ;'f' \\1000:00403538 46 db 69 ;'i' \\1000:00403539 69 db 65 ;'e' \\1000:0040353a 65 db 6c ;'l' \\1000:0040353b 6c db 64 ;'d' \\1000:0040353c 64 db 00 \\1000:0040353d 00 could not fit the rest in because of Stack overflow limitions

  Read the article

• while I scroll between the layout it takes too long to be able to scroll between the gallerie's pictures. Is there any way to reduce this time?

  - by Mateo

  Hello, this is my first question here, though I've being reading this forum for quite a while. Most of the answers to my doubts are from here :) Getting back on topic. I'm developing an Android application. I'm drawing a dynamic layout that are basically Galleries, inside a LinearLayout, inside a ScrollView, inside a RelativeLayout. The ScrollView is a must, because I'm drawing a dynamic amount of galleries that most probably will not fit on the screen. When I scroll inside the layout, I have to wait 3/4 seconds until the ScrollView "deactivates" to be able to scroll inside the galleries. What I want to do is to reduce this time to a minimum. Preferably I would like to be able to scroll inside the galleries as soon as I lift my finger from the screen, though anything lower than 2 seconds would be great as well. I've being googling around for a solution but all I could find until now where layout tutorials that didn't tackle this particular issue. I was hoping someone here knows if this is possible and if so to give me some hints on how to do so. I would prefer not to do my own ScrollView to solve this. But if that is the only way I would appreciate some help because I'm not really sure how would I solve this issue by doing that. this is my layout: public class PicturesL extends Activity implements OnClickListener, OnItemClickListener, OnItemLongClickListener { private ArrayList<ImageView> imageView = new ArrayList<ImageView>(); private StringBuilder PicsDate = new StringBuilder(); private CaWaApplication application; private long ListID; private ArrayList<Gallery> gallery = new ArrayList<Gallery>(); private ArrayList<Bitmap> Thumbails = new ArrayList<Bitmap>(); private String idioma; private ArrayList<Long> Days = new ArrayList<Long>(); private long oldDay; private long oldThumbsLoaded; private ArrayList<Long> ThumbailsDays = new ArrayList<Long>(); private ArrayList<ArrayList<Long>> IDs = new ArrayList<ArrayList<Long>>(); @Override public void onCreate(Bundle savedInstancedState) { super.onCreate(savedInstancedState); RelativeLayout layout = new RelativeLayout(this); ScrollView scroll = new ScrollView(this); LinearLayout realLayout = new LinearLayout(this); ArrayList<TextView> texts = new ArrayList<TextView>(); Button TakePic = new Button(this); idioma = com.mateloft.cawa.prefs.getLang(this); if (idioma.equals("en")) { TakePic.setText("Take Picture"); } else if (idioma.equals("es")) { TakePic.setText("Sacar Foto"); } RelativeLayout.LayoutParams scrollLP = new RelativeLayout.LayoutParams(RelativeLayout.LayoutParams.FILL_PARENT, RelativeLayout.LayoutParams.FILL_PARENT); layout.addView(scroll, scrollLP); realLayout.setOrientation(LinearLayout.VERTICAL); realLayout.setLayoutParams(new LayoutParams(LayoutParams.FILL_PARENT, LayoutParams.FILL_PARENT)); scroll.addView(realLayout); TakePic.setId(67); TakePic.setOnClickListener(this); application = (CaWaApplication) getApplication(); ListID = getIntent().getExtras().getLong("listid"); getAllThumbailsOfID(); LinearLayout.LayoutParams TakeLP = new LinearLayout.LayoutParams(LinearLayout.LayoutParams.WRAP_CONTENT, LinearLayout.LayoutParams.WRAP_CONTENT); realLayout.addView(TakePic); oldThumbsLoaded = 0; int galler = 100; for (int z = 0; z < Days.size(); z++) { ThumbailsManager croppedThumbs = new ThumbailsManager(Thumbails, oldThumbsLoaded, ThumbailsDays.get(z)); oldThumbsLoaded = ThumbailsDays.get(z); texts.add(new TextView(this)); texts.get(z).setText("Day " + Days.get(z).toString()); gallery.add(new Gallery(this)); gallery.get(z).setAdapter(new ImageAdapter(this, croppedThumbs.getGallery(), 250, 175, true, ListID)); gallery.get(z).setOnItemClickListener(this); gallery.get(z).setOnItemLongClickListener(this); gallery.get(z).setId(galler); galler++; realLayout.addView(texts.get(z)); realLayout.addView(gallery.get(z)); } Log.d("PicturesL", "ListID: " + ListID); setContentView(layout); } private void getAllThumbailsOfID() { ArrayList<ModelPics> Pictures = new ArrayList<ModelPics>(); ArrayList<String> ThumbailsPath = new ArrayList<String>(); Pictures = application.dataManager.selectAllPics(); long thumbpathloaded = 0; int currentID = 0; for (int x = 0; x < Pictures.size(); x++) { if (Pictures.get(x).walkname == ListID) { if (Days.size() == 0) { Days.add(Pictures.get(x).day); oldDay = Pictures.get(x).day; IDs.add(new ArrayList<Long>()); currentID = 0; } if (oldDay != Pictures.get(x).day) { oldDay = Pictures.get(x).day; ThumbailsDays.add(thumbpathloaded); Days.add(Pictures.get(x).day); IDs.add(new ArrayList<Long>()); currentID++; } StringBuilder tpath = new StringBuilder(); tpath.append(Pictures.get(x).path.substring(0, Pictures.get(x).path.length() - 4)); tpath.append("-t.jpg"); IDs.get(currentID).add(Pictures.get(x).id); ThumbailsPath.add(tpath.toString()); thumbpathloaded++; if (x == Pictures.size() - 1) { Log.d("PicturesL", "El ultimo de los arrays, tamaño: " + Days.size()); ThumbailsDays.add(thumbpathloaded); } } } for (int y = 0; y < ThumbailsPath.size(); y++) { Thumbails.add(BitmapFactory.decodeFile(ThumbailsPath.get(y))); } } I had a memory leak on another activity when screen orientation changed that was making it slower, now it is working better. The scroller is not locking up. But sometimes, when it stops scrolling, it takes a few seconds (2/3) to disable itself. I just want it to be a little more dynamic, is there any way to override the listener and make it stop scrolling ON_ACTION_UP or something like that? I don't want to use the listview because I want to have each gallery separated by other views, now I just have text, but I will probably separate them with images with a different size than the galleries. I'm not really sure if this is possible with a listadapter and a listview, I assumed that a view can only handle only one type of object, so I'm using a scrollview of a layout, if I'm wrong please correct me :) Also this activity works as a preview or selecting the pictures you want to view in full size and manage their values. So its working only with thumbnails. Each one weights 40 kb. Guessing that is very unlikely that a user gets more than 1000~1500 pictures in this view, i thought that the activity wouldn't use more than 40~50 mb of ram in this case, adding 10 more if I open the fullsized view. So I guessed as well most devices are able to display this view in full size. If it doesn't work on low-end devices my plan was to add an option in the app preferences to let user chop this view according to some database values. And a last reason is that during most of this activity "life-cycle" (the app has pics that are relevant to the view, when it ends the value that selects which pictures are displayed has to change and no more pictures are added inside this instance of this activity); the view will be unpopulated, so most of the time showing everything wont cost much, just at the end of its cycle That was more or less what I thought at the time i created this layout. I'm open to any sort of suggestion or opinion, I just created this layout a few days ago and I'm trying to see if it can work right, because it suits my app needs. Though if there is a better way i would love to hear it Thanks Mateo

  Read the article

< Previous Page | 1 2 3 4