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  • Recommendations for distributed processing/distributed storage systems

    - by Eddie
    At my organization we have a processing and storage system spread across two dozen linux machines that handles over a petabyte of data. The system right now is very ad-hoc; processing automation and data management is handled by a collection of large perl programs on independent machines. I am looking at distributed processing and storage systems to make it easier to maintain, evenly distribute load and data with replication, and grow in disk space and compute power. The system needs to be able to handle millions of files, varying in size between 50 megabytes to 50 gigabytes. Once created, the files will not be appended to, only replaced completely if need be. The files need to be accessible via HTTP for customer download. Right now, processing is automated by perl scripts (that I have complete control over) which call a series of other programs (that I don't have control over because they are closed source) that essentially transforms one data set into another. No data mining happening here. Here is a quick list of things I am looking for: Reliability: These data must be accessible over HTTP about 99% of the time so I need something that does data replication across the cluster. Scalability: I want to be able to add more processing power and storage easily and rebalance the data on across the cluster. Distributed processing: Easy and automatic job scheduling and load balancing that fits with processing workflow I briefly described above. Data location awareness: Not strictly required but desirable. Since data and processing will be on the same set of nodes I would like the job scheduler to schedule jobs on or close to the node that the data is actually on to cut down on network traffic. Here is what I've looked at so far: Storage Management: GlusterFS: Looks really nice and easy to use but doesn't seem to have a way to figure out what node(s) a file actually resides on to supply as a hint to the job scheduler. GPFS: Seems like the gold standard of clustered filesystems. Meets most of my requirements except, like glusterfs, data location awareness. Ceph: Seems way to immature right now. Distributed processing: Sun Grid Engine: I have a lot of experience with this and it's relatively easy to use (once it is configured properly that is). But Oracle got its icy grip around it and it no longer seems very desirable. Both: Hadoop/HDFS: At first glance it looked like hadoop was perfect for my situation. Distributed storage and job scheduling and it was the only thing I found that would give me the data location awareness that I wanted. But I don't like the namename being a single point of failure. Also, I'm not really sure if the MapReduce paradigm fits the type of processing workflow that I have. It seems like you need to write all your software specifically for MapReduce instead of just using Hadoop as a generic job scheduler. OpenStack: I've done some reading on this but I'm having trouble deciding if it fits well with my problem or not. Does anyone have opinions or recommendations for technologies that would fit my problem well? Any suggestions or advise would be greatly appreciated. Thanks!

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  • Building a Redundant / Distributed Application

    - by MattW
    This is more of a "point me in the right direction" question. My team of three and I have built a hosted web app that queues and routes customer chat requests to available customer service agents (It does other things as well, but this is enough background to illustrate the issue). The basic dev architecture today is: a single page ajax web UI (ASP.NET MVC) with floating chat windows (think Gmail) a backend Windows service to queue and route the chat requests this service also logs the chats, calculates service levels, etc a Comet server product that routes data between the web frontend and the backend Windows service this also helps us detect which Agents are still connected (online) And our hardware architecture today is: 2 servers to host the web UI portion of the application a load balancer to route requests to the 2 different web app servers a third server to host the SQL Server DB and the backend Windows service responsible for queuing / delivering chats So as it stands today, one of the web app servers could go down and we would be ok. However, if something would happen to the SQL Server / Windows Service server we would be boned. My question - how can I make this backend Windows service logic be able to be spread across multiple machines (distributed)? The Windows service is written to accept requests from the Comet server, check for available Agents, and route the chat to those agents. How can I make this more distributed? How can I make it so that I can distribute the work of the backend Windows service can be spread across multiple machines for redundancy and uptime purposes? Will I need to re-write it with distributed computing in mind? I should also note that I am hosting all of this on Rackspace Cloud instances - so maybe it is something I should be less concerned about? Thanks in advance for any help!

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  • Java - System design with distributed Queues and Locks

    - by sunny
    Looking for inputs to evaluate a design for a system (java) which would have a distributed queue serving several (but not too many) nodes. These nodes would process objects present in the distributed queue and on occasion require a distributed lock across the cluster on an arbitrary (distributed) data structures. These (distributed) data structures could potentially lie in a distributed cache. Eliminating Terracotta (DSO),Hazelcast and Akka what could be alternative choices. Currently considering zookeeper as a distributed locking mechanism. Since the recommendation of a znode is not to exceed the 1M size , the understanding is that zookeeper should not be used a distributed queue. And also from Netflix curator tech note 4. So should a distributed cache, say like memcached, or redis be used to emulate a distributed queue ? i.e. The distributed queue will be stored in the caches and will be locked cluster-wide via zookeeper. Are there potential pitfalls with this high-level approach. The objects don't need to be taken off the queue. The object will pass through a lifecycle which will determine its removal from the queue. There would be about 10k+ objects in a queue at a given time changing states and any node could service one stage of the object's lifecycle. (Although not strictly necessary .. i.e. one node could serve the entire lifecycle if that is more efficient.) Any suggestions/alternatives ? sidenote: new to zookeeper ; redis etc.

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  • Distributed storage and computing

    - by Tim van Elteren
    Dear Serverfault community, After researching a number of distributed file systems for deployment in a production environment with the main purpose of performing both batch and real-time distributed computing I've identified the following list as potential candidates, mainly on maturity, license and support: Ceph Lustre GlusterFS HDFS FhGFS MooseFS XtreemFS The key properties that our system should exhibit: an open source, liberally licensed, yet production ready, e.g. a mature, reliable, community and commercially supported solution; ability to run on commodity hardware, preferably be designed for it; provide high availability of the data with the most focus on reads; high scalability, so operation over multiple data centres, possibly on a global scale; removal of single points of failure with the use of replication and distribution of (meta-)data, e.g. provide fault-tolerance. The sensitivity points that were identified, and resulted in the following questions, are: transparency to the processing layer / application with respect to data locality, e.g. know where data is physically located on a server level, mainly for resource allocation and fast processing, high performance, how can this be accomplished? Do you from experience know what solutions provide this transparency and to what extent? posix compliance, or conformance, is mentioned on the wiki pages of most of the above listed solutions. The question here mainly is, how relevant is support for the posix standard? Hadoop for example isn't posix compliant by design, what are the pro's and con's? what about the difference between synchronous and asynchronous opeartion of a distributed file system. Though a synchronous distributed file system has the preference because of reliability it also imposes certain limitations with respect to scalability. What would be, from your expertise, the way to go on this? I'm looking forward to your replies. Thanks in advance! :) With kind regards, Tim van Elteren

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  • Ancillary Objects: Separate Debug ELF Files For Solaris

    - by Ali Bahrami
    We introduced a new object ELF object type in Solaris 11 Update 1 called the Ancillary Object. This posting describes them, using material originally written during their development, the PSARC arc case, and the Solaris Linker and Libraries Manual. ELF objects contain allocable sections, which are mapped into memory at runtime, and non-allocable sections, which are present in the file for use by debuggers and observability tools, but which are not mapped or used at runtime. Typically, all of these sections exist within a single object file. Ancillary objects allow them to instead go into a separate file. There are different reasons given for wanting such a feature. One can debate whether the added complexity is worth the benefit, and in most cases it is not. However, one important case stands out — customers with very large 32-bit objects who are not ready or able to make the transition to 64-bits. We have customers who build extremely large 32-bit objects. Historically, the debug sections in these objects have used the stabs format, which is limited, but relatively compact. In recent years, the industry has transitioned to the powerful but verbose DWARF standard. In some cases, the size of these debug sections is large enough to push the total object file size past the fundamental 4GB limit for 32-bit ELF object files. The best, and ultimately only, solution to overly large objects is to transition to 64-bits. However, consider environments where: Hundreds of users may be executing the code on large shared systems. (32-bits use less memory and bus bandwidth, and on sparc runs just as fast as 64-bit code otherwise). Complex finely tuned code, where the original authors may no longer be available. Critical production code, that was expensive to qualify and bring online, and which is otherwise serving its intended purpose without issue. Users in these risk adverse and/or high scale categories have good reasons to push 32-bits objects to the limit before moving on. Ancillary objects offer these users a longer runway. Design The design of ancillary objects is intended to be simple, both to help human understanding when examining elfdump output, and to lower the bar for debuggers such as dbx to support them. The primary and ancillary objects have the same set of section headers, with the same names, in the same order (i.e. each section has the same index in both files). A single added section of type SHT_SUNW_ANCILLARY is added to both objects, containing information that allows a debugger to identify and validate both files relative to each other. Given one of these files, the ancillary section allows you to identify the other. Allocable sections go in the primary object, and non-allocable ones go into the ancillary object. A small set of non-allocable objects, notably the symbol table, are copied into both objects. As noted above, most sections are only written to one of the two objects, but both objects have the same section header array. The section header in the file that does not contain the section data is tagged with the SHF_SUNW_ABSENT section header flag to indicate its placeholder status. Compiler writers and others who produce objects can set the SUNW_SHF_PRIMARY section header flag to mark non-allocable sections that should go to the primary object rather than the ancillary. If you don't request an ancillary object, the Solaris ELF format is unchanged. Users who don't use ancillary objects do not pay for the feature. This is important, because they exist to serve a small subset of our users, and must not complicate the common case. If you do request an ancillary object, the runtime behavior of the primary object will be the same as that of a normal object. There is no added runtime cost. The primary and ancillary object together represent a logical single object. This is facilitated by the use of a single set of section headers. One can easily imagine a tool that can merge a primary and ancillary object into a single file, or the reverse. (Note that although this is an interesting intellectual exercise, we don't actually supply such a tool because there's little practical benefit above and beyond using ld to create the files). Among the benefits of this approach are: There is no need for per-file symbol tables to reflect the contents of each file. The same symbol table that would be produced for a standard object can be used. The section contents are identical in either case — there is no need to alter data to accommodate multiple files. It is very easy for a debugger to adapt to these new files, and the processing involved can be encapsulated in input/output routines. Most of the existing debugger implementation applies without modification. The limit of a 4GB 32-bit output object is now raised to 4GB of code, and 4GB of debug data. There is also the future possibility (not currently supported) to support multiple ancillary objects, each of which could contain up to 4GB of additional debug data. It must be noted however that the 32-bit DWARF debug format is itself inherently 32-bit limited, as it uses 32-bit offsets between debug sections, so the ability to employ multiple ancillary object files may not turn out to be useful. Using Ancillary Objects (From the Solaris Linker and Libraries Guide) By default, objects contain both allocable and non-allocable sections. Allocable sections are the sections that contain executable code and the data needed by that code at runtime. Non-allocable sections contain supplemental information that is not required to execute an object at runtime. These sections support the operation of debuggers and other observability tools. The non-allocable sections in an object are not loaded into memory at runtime by the operating system, and so, they have no impact on memory use or other aspects of runtime performance no matter their size. For convenience, both allocable and non-allocable sections are normally maintained in the same file. However, there are situations in which it can be useful to separate these sections. To reduce the size of objects in order to improve the speed at which they can be copied across wide area networks. To support fine grained debugging of highly optimized code requires considerable debug data. In modern systems, the debugging data can easily be larger than the code it describes. The size of a 32-bit object is limited to 4 Gbytes. In very large 32-bit objects, the debug data can cause this limit to be exceeded and prevent the creation of the object. To limit the exposure of internal implementation details. Traditionally, objects have been stripped of non-allocable sections in order to address these issues. Stripping is effective, but destroys data that might be needed later. The Solaris link-editor can instead write non-allocable sections to an ancillary object. This feature is enabled with the -z ancillary command line option. $ ld ... -z ancillary[=outfile] ...By default, the ancillary file is given the same name as the primary output object, with a .anc file extension. However, a different name can be provided by providing an outfile value to the -z ancillary option. When -z ancillary is specified, the link-editor performs the following actions. All allocable sections are written to the primary object. In addition, all non-allocable sections containing one or more input sections that have the SHF_SUNW_PRIMARY section header flag set are written to the primary object. All remaining non-allocable sections are written to the ancillary object. The following non-allocable sections are written to both the primary object and ancillary object. .shstrtab The section name string table. .symtab The full non-dynamic symbol table. .symtab_shndx The symbol table extended index section associated with .symtab. .strtab The non-dynamic string table associated with .symtab. .SUNW_ancillary Contains the information required to identify the primary and ancillary objects, and to identify the object being examined. The primary object and all ancillary objects contain the same array of sections headers. Each section has the same section index in every file. Although the primary and ancillary objects all define the same section headers, the data for most sections will be written to a single file as described above. If the data for a section is not present in a given file, the SHF_SUNW_ABSENT section header flag is set, and the sh_size field is 0. This organization makes it possible to acquire a full list of section headers, a complete symbol table, and a complete list of the primary and ancillary objects from either of the primary or ancillary objects. The following example illustrates the underlying implementation of ancillary objects. An ancillary object is created by adding the -z ancillary command line option to an otherwise normal compilation. The file utility shows that the result is an executable named a.out, and an associated ancillary object named a.out.anc. $ cat hello.c #include <stdio.h> int main(int argc, char **argv) { (void) printf("hello, world\n"); return (0); } $ cc -g -zancillary hello.c $ file a.out a.out.anc a.out: ELF 32-bit LSB executable 80386 Version 1 [FPU], dynamically linked, not stripped, ancillary object a.out.anc a.out.anc: ELF 32-bit LSB ancillary 80386 Version 1, primary object a.out $ ./a.out hello worldThe resulting primary object is an ordinary executable that can be executed in the usual manner. It is no different at runtime than an executable built without the use of ancillary objects, and then stripped of non-allocable content using the strip or mcs commands. As previously described, the primary object and ancillary objects contain the same section headers. To see how this works, it is helpful to use the elfdump utility to display these section headers and compare them. The following table shows the section header information for a selection of headers from the previous link-edit example. Index Section Name Type Primary Flags Ancillary Flags Primary Size Ancillary Size 13 .text PROGBITS ALLOC EXECINSTR ALLOC EXECINSTR SUNW_ABSENT 0x131 0 20 .data PROGBITS WRITE ALLOC WRITE ALLOC SUNW_ABSENT 0x4c 0 21 .symtab SYMTAB 0 0 0x450 0x450 22 .strtab STRTAB STRINGS STRINGS 0x1ad 0x1ad 24 .debug_info PROGBITS SUNW_ABSENT 0 0 0x1a7 28 .shstrtab STRTAB STRINGS STRINGS 0x118 0x118 29 .SUNW_ancillary SUNW_ancillary 0 0 0x30 0x30 The data for most sections is only present in one of the two files, and absent from the other file. The SHF_SUNW_ABSENT section header flag is set when the data is absent. The data for allocable sections needed at runtime are found in the primary object. The data for non-allocable sections used for debugging but not needed at runtime are placed in the ancillary file. A small set of non-allocable sections are fully present in both files. These are the .SUNW_ancillary section used to relate the primary and ancillary objects together, the section name string table .shstrtab, as well as the symbol table.symtab, and its associated string table .strtab. It is possible to strip the symbol table from the primary object. A debugger that encounters an object without a symbol table can use the .SUNW_ancillary section to locate the ancillary object, and access the symbol contained within. The primary object, and all associated ancillary objects, contain a .SUNW_ancillary section that allows all the objects to be identified and related together. $ elfdump -T SUNW_ancillary a.out a.out.anc a.out: Ancillary Section: .SUNW_ancillary index tag value [0] ANC_SUNW_CHECKSUM 0x8724 [1] ANC_SUNW_MEMBER 0x1 a.out [2] ANC_SUNW_CHECKSUM 0x8724 [3] ANC_SUNW_MEMBER 0x1a3 a.out.anc [4] ANC_SUNW_CHECKSUM 0xfbe2 [5] ANC_SUNW_NULL 0 a.out.anc: Ancillary Section: .SUNW_ancillary index tag value [0] ANC_SUNW_CHECKSUM 0xfbe2 [1] ANC_SUNW_MEMBER 0x1 a.out [2] ANC_SUNW_CHECKSUM 0x8724 [3] ANC_SUNW_MEMBER 0x1a3 a.out.anc [4] ANC_SUNW_CHECKSUM 0xfbe2 [5] ANC_SUNW_NULL 0 The ancillary sections for both objects contain the same number of elements, and are identical except for the first element. Each object, starting with the primary object, is introduced with a MEMBER element that gives the file name, followed by a CHECKSUM that identifies the object. In this example, the primary object is a.out, and has a checksum of 0x8724. The ancillary object is a.out.anc, and has a checksum of 0xfbe2. The first element in a .SUNW_ancillary section, preceding the MEMBER element for the primary object, is always a CHECKSUM element, containing the checksum for the file being examined. The presence of a .SUNW_ancillary section in an object indicates that the object has associated ancillary objects. The names of the primary and all associated ancillary objects can be obtained from the ancillary section from any one of the files. It is possible to determine which file is being examined from the larger set of files by comparing the first checksum value to the checksum of each member that follows. Debugger Access and Use of Ancillary Objects Debuggers and other observability tools must merge the information found in the primary and ancillary object files in order to build a complete view of the object. This is equivalent to processing the information from a single file. This merging is simplified by the primary object and ancillary objects containing the same section headers, and a single symbol table. The following steps can be used by a debugger to assemble the information contained in these files. Starting with the primary object, or any of the ancillary objects, locate the .SUNW_ancillary section. The presence of this section identifies the object as part of an ancillary group, contains information that can be used to obtain a complete list of the files and determine which of those files is the one currently being examined. Create a section header array in memory, using the section header array from the object being examined as an initial template. Open and read each file identified by the .SUNW_ancillary section in turn. For each file, fill in the in-memory section header array with the information for each section that does not have the SHF_SUNW_ABSENT flag set. The result will be a complete in-memory copy of the section headers with pointers to the data for all sections. Once this information has been acquired, the debugger can proceed as it would in the single file case, to access and control the running program. Note - The ELF definition of ancillary objects provides for a single primary object, and an arbitrary number of ancillary objects. At this time, the Oracle Solaris link-editor only produces a single ancillary object containing all non-allocable sections. This may change in the future. Debuggers and other observability tools should be written to handle the general case of multiple ancillary objects. ELF Implementation Details (From the Solaris Linker and Libraries Guide) To implement ancillary objects, it was necessary to extend the ELF format to add a new object type (ET_SUNW_ANCILLARY), a new section type (SHT_SUNW_ANCILLARY), and 2 new section header flags (SHF_SUNW_ABSENT, SHF_SUNW_PRIMARY). In this section, I will detail these changes, in the form of diffs to the Solaris Linker and Libraries manual. Part IV ELF Application Binary Interface Chapter 13: Object File Format Object File Format Edit Note: This existing section at the beginning of the chapter describes the ELF header. There's a table of object file types, which now includes the new ET_SUNW_ANCILLARY type. e_type Identifies the object file type, as listed in the following table. NameValueMeaning ET_NONE0No file type ET_REL1Relocatable file ET_EXEC2Executable file ET_DYN3Shared object file ET_CORE4Core file ET_LOSUNW0xfefeStart operating system specific range ET_SUNW_ANCILLARY0xfefeAncillary object file ET_HISUNW0xfefdEnd operating system specific range ET_LOPROC0xff00Start processor-specific range ET_HIPROC0xffffEnd processor-specific range Sections Edit Note: This overview section defines the section header structure, and provides a high level description of known sections. It was updated to define the new SHF_SUNW_ABSENT and SHF_SUNW_PRIMARY flags and the new SHT_SUNW_ANCILLARY section. ... sh_type Categorizes the section's contents and semantics. Section types and their descriptions are listed in Table 13-5. sh_flags Sections support 1-bit flags that describe miscellaneous attributes. Flag definitions are listed in Table 13-8. ... Table 13-5 ELF Section Types, sh_type NameValue . . . SHT_LOSUNW0x6fffffee SHT_SUNW_ancillary0x6fffffee . . . ... SHT_LOSUNW - SHT_HISUNW Values in this inclusive range are reserved for Oracle Solaris OS semantics. SHT_SUNW_ANCILLARY Present when a given object is part of a group of ancillary objects. Contains information required to identify all the files that make up the group. See Ancillary Section. ... Table 13-8 ELF Section Attribute Flags NameValue . . . SHF_MASKOS0x0ff00000 SHF_SUNW_NODISCARD0x00100000 SHF_SUNW_ABSENT0x00200000 SHF_SUNW_PRIMARY0x00400000 SHF_MASKPROC0xf0000000 . . . ... SHF_SUNW_ABSENT Indicates that the data for this section is not present in this file. When ancillary objects are created, the primary object and any ancillary objects, will all have the same section header array, to facilitate merging them to form a complete view of the object, and to allow them to use the same symbol tables. Each file contains a subset of the section data. The data for allocable sections is written to the primary object while the data for non-allocable sections is written to an ancillary file. The SHF_SUNW_ABSENT flag is used to indicate that the data for the section is not present in the object being examined. When the SHF_SUNW_ABSENT flag is set, the sh_size field of the section header must be 0. An application encountering an SHF_SUNW_ABSENT section can choose to ignore the section, or to search for the section data within one of the related ancillary files. SHF_SUNW_PRIMARY The default behavior when ancillary objects are created is to write all allocable sections to the primary object and all non-allocable sections to the ancillary objects. The SHF_SUNW_PRIMARY flag overrides this behavior. Any output section containing one more input section with the SHF_SUNW_PRIMARY flag set is written to the primary object without regard for its allocable status. ... Two members in the section header, sh_link, and sh_info, hold special information, depending on section type. Table 13-9 ELF sh_link and sh_info Interpretation sh_typesh_linksh_info . . . SHT_SUNW_ANCILLARY The section header index of the associated string table. 0 . . . Special Sections Edit Note: This section describes the sections used in Solaris ELF objects, using the types defined in the previous description of section types. It was updated to define the new .SUNW_ancillary (SHT_SUNW_ANCILLARY) section. Various sections hold program and control information. Sections in the following table are used by the system and have the indicated types and attributes. Table 13-10 ELF Special Sections NameTypeAttribute . . . .SUNW_ancillarySHT_SUNW_ancillaryNone . . . ... .SUNW_ancillary Present when a given object is part of a group of ancillary objects. Contains information required to identify all the files that make up the group. See Ancillary Section for details. ... Ancillary Section Edit Note: This new section provides the format reference describing the layout of a .SUNW_ancillary section and the meaning of the various tags. Note that these sections use the same tag/value concept used for dynamic and capabilities sections, and will be familiar to anyone used to working with ELF. In addition to the primary output object, the Solaris link-editor can produce one or more ancillary objects. Ancillary objects contain non-allocable sections that would normally be written to the primary object. When ancillary objects are produced, the primary object and all of the associated ancillary objects contain a SHT_SUNW_ancillary section, containing information that identifies these related objects. Given any one object from such a group, the ancillary section provides the information needed to identify and interpret the others. This section contains an array of the following structures. See sys/elf.h. typedef struct { Elf32_Word a_tag; union { Elf32_Word a_val; Elf32_Addr a_ptr; } a_un; } Elf32_Ancillary; typedef struct { Elf64_Xword a_tag; union { Elf64_Xword a_val; Elf64_Addr a_ptr; } a_un; } Elf64_Ancillary; For each object with this type, a_tag controls the interpretation of a_un. a_val These objects represent integer values with various interpretations. a_ptr These objects represent file offsets or addresses. The following ancillary tags exist. Table 13-NEW1 ELF Ancillary Array Tags NameValuea_un ANC_SUNW_NULL0Ignored ANC_SUNW_CHECKSUM1a_val ANC_SUNW_MEMBER2a_ptr ANC_SUNW_NULL Marks the end of the ancillary section. ANC_SUNW_CHECKSUM Provides the checksum for a file in the c_val element. When ANC_SUNW_CHECKSUM precedes the first instance of ANC_SUNW_MEMBER, it provides the checksum for the object from which the ancillary section is being read. When it follows an ANC_SUNW_MEMBER tag, it provides the checksum for that member. ANC_SUNW_MEMBER Specifies an object name. The a_ptr element contains the string table offset of a null-terminated string, that provides the file name. An ancillary section must always contain an ANC_SUNW_CHECKSUM before the first instance of ANC_SUNW_MEMBER, identifying the current object. Following that, there should be an ANC_SUNW_MEMBER for each object that makes up the complete set of objects. Each ANC_SUNW_MEMBER should be followed by an ANC_SUNW_CHECKSUM for that object. A typical ancillary section will therefore be structured as: TagMeaning ANC_SUNW_CHECKSUMChecksum of this object ANC_SUNW_MEMBERName of object #1 ANC_SUNW_CHECKSUMChecksum for object #1 . . . ANC_SUNW_MEMBERName of object N ANC_SUNW_CHECKSUMChecksum for object N ANC_SUNW_NULL An object can therefore identify itself by comparing the initial ANC_SUNW_CHECKSUM to each of the ones that follow, until it finds a match. Related Other Work The GNU developers have also encountered the need/desire to support separate debug information files, and use the solution detailed at http://sourceware.org/gdb/onlinedocs/gdb/Separate-Debug-Files.html. At the current time, the separate debug file is constructed by building the standard object first, and then copying the debug data out of it in a separate post processing step, Hence, it is limited to a total of 4GB of code and debug data, just as a single object file would be. They are aware of this, and I have seen online comments indicating that they may add direct support for generating these separate files to their link-editor. It is worth noting that the GNU objcopy utility is available on Solaris, and that the Studio dbx debugger is able to use these GNU style separate debug files even on Solaris. Although this is interesting in terms giving Linux users a familiar environment on Solaris, the 4GB limit means it is not an answer to the problem of very large 32-bit objects. We have also encountered issues with objcopy not understanding Solaris-specific ELF sections, when using this approach. The GNU community also has a current effort to adapt their DWARF debug sections in order to move them to separate files before passing the relocatable objects to the linker. The details of Project Fission can be found at http://gcc.gnu.org/wiki/DebugFission. The goal of this project appears to be to reduce the amount of data seen by the link-editor. The primary effort revolves around moving DWARF data to separate .dwo files so that the link-editor never encounters them. The details of modifying the DWARF data to be usable in this form are involved — please see the above URL for details.

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  • Elastic versus Distributed in caching.

    - by Mike Reys
    Until now, I hadn't heard about Elastic Caching yet. Today I read Mike Gualtieri's Blog entry. I immediately thought about Oracle Coherence and got a little scare throughout the reading. Elastic Caching is the next step after Distributed Caching. As we've always positioned Coherence as a Distributed Cache, I thought for a brief instance that Oracle had missed a new trend/technology. But then I started reading the characteristics of an Elastic Cache. Forrester definition: Software infrastructure that provides application developers with data caching services that are distributed across two or more server nodes that consistently perform as volumes grow can be scaled without downtime provide a range of fault-tolerance levels Hey wait a minute, doesn't Coherence fullfill all these requirements? Oh yes, I think it does! The next defintion in the article is about Elastic Application Platforms. This is mainly more of the same with the addition of code execution. Now there is analytics functionality in Oracle Coherence. The analytics capability provides data-centric functions like distributed aggregation, searching and sorting. Coherence also provides continuous querying and event-handling. I think that when it comes to providing an Elastic Application Platform (as in the Forrester definition), Oracle is close, nearly there. And what's more, as Elastic Platform is the next big thing towards the big C word, Oracle Coherence makes you cloud-ready ;-) There you go! Find more info on Oracle Coherence here.

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  • Building a distributed system on Amazon Web Services

    - by Songo
    Would simply using AWS to build an application make this application a distributed system? For example if someone uses RDS for the database server, EC2 for the application itself and S3 for hosting user uploaded media, does that make it a distributed system? If not, then what should it be called and what is this application lacking for it to be distributed? Update Here is my take on the application to clarify my approach to building the system: The application I'm building is a social game for Facebook. I developed the application locally on a LAMP stack using Symfony2. For production I used an a single EC2 Micro instance for hosting the app itself, RDS for hosting my database, S3 for the user uploaded files and CloudFront for hosting static content. I know this may sound like a naive approach, so don't be shy to express your ideas.

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  • Design a Distributed System

    - by Bonton255
    I am preparing for an interview on Distributed Systems. I have gone through a lot of text and understand the basics of the area. However, I need some examples of discussions on designing a distributed system given a scenario. For example, if I were to design a distributed system to calculate if a number N is primary or not, what will the be design of the system, what will be the impact of network latency, CPU performance, node failure, addition of nodes, time synchronization etc. If you guys could present your in-depth thoughts on this example, or point me to some similar discussion, that would be really helpful.

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  • Can an issue tracking system be distributed?

    - by Klaim
    I was thinking about issue tracking software like Redmine, Trac or even the one that is in Fossil and something hit me: Is there a reason why Redmine and Trac are not possible to be distributed? Or maybe it's possible and I just don't know how it's possible? If it's not possible, why? By distributed I mean like Facebook or Google or other applications that effectively runs on multiple hardware a the same time but share data.

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  • Distributed Computing Framework (.NET) - Specifically for CPU Instensive operations

    - by StevenH
    I am currently researching the options that are available (both Open Source and Commercial) for developing a distributed application. "A distributed system consists of multiple autonomous computers that communicate through a computer network." Wikipedia The application is focused on distributing highly cpu intensive operations (as opposed to data intensive) so I'm sure MapReduce solutions don't fit the bill. Any framework that you can recommend ( + give a brief summary of any experience or comparison to other frameworks ) would be greatly appreciated. Thanks.

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  • Distributed Development Tools -- (Version control and Project Management)

    - by Macy Abbey
    Hello, I've recently become responsible for choosing which source control and project management software to use for a company that employs me. Currently it uses Jira (project management) and Subversion (version control). I know there are many other options out there -- the ones I know about are all in this article http://mashable.com/2010/07/14/distributed-developer-teams/ . I'm leaning towards recommending they just stay with what they have as it seems workable and any change would have to be worth the cost of switching to say github/basecamp or some other solution. Some details on the team: It's a distributed development shop. Meetings of the whole team in one room are rare. It's currently a very small development team (three developers). The project management software is used by developers and a product manager or two. What are you experiences with version control and project management web applications? Are there any you would recommend and you think are worth the switching cost of time to learn new services / implementing the change? Edit: After educating myself further on the options it appears DVCS offer powerful benefits that may be worth investing in now as opposed to later in the company's lifetime when the switching cost is higher: I'm a Subversion geek, why I should consider or not consider Mercurial or Git or any other DVCS?

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  • Distributed Development Tools -- (Version control and Project Management)

    - by Macy Abbey
    I've recently become responsible for choosing which source control and project management software to use for a company that employs me. Currently it uses Jira (project management) and Subversion (version control). I know there are many other options out there -- the ones I know about are all in this article http://mashable.com/2010/07/14/distributed-developer-teams/ . I'm leaning towards recommending they just stay with what they have as it seems workable and any change would have to be worth the cost of switching to say github/basecamp or some other solution. Some details on the team: It's a distributed development shop. Meetings of the whole team in one room are rare. It's currently a very small development team (three developers). The project management software is used by developers and a product manager or two. What are you experiences with version control and project management web applications? Are there any you would recommend and you think are worth the switching cost of time to learn new services / implementing the change? Edit: After educating myself further on the options it appears DVCS offer powerful benefits that may be worth investing in now as opposed to later in the company's lifetime when the switching cost is higher: I'm a Subversion geek, why I should consider or not consider Mercurial or Git or any other DVCS?

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  • Much Ado About Nothing: Stub Objects

    - by user9154181
    The Solaris 11 link-editor (ld) contains support for a new type of object that we call a stub object. A stub object is a shared object, built entirely from mapfiles, that supplies the same linking interface as the real object, while containing no code or data. Stub objects cannot be executed — the runtime linker will kill any process that attempts to load one. However, you can link to a stub object as a dependency, allowing the stub to act as a proxy for the real version of the object. You may well wonder if there is a point to producing an object that contains nothing but linking interface. As it turns out, stub objects are very useful for building large bodies of code such as Solaris. In the last year, we've had considerable success in applying them to one of our oldest and thorniest build problems. In this discussion, I will describe how we came to invent these objects, and how we apply them to building Solaris. This posting explains where the idea for stub objects came from, and details our long and twisty journey from hallway idea to standard link-editor feature. I expect that these details are mainly of interest to those who work on Solaris and its makefiles, those who have done so in the past, and those who work with other similar bodies of code. A subsequent posting will omit the history and background details, and instead discuss how to build and use stub objects. If you are mainly interested in what stub objects are, and don't care about the underlying software war stories, I encourage you to skip ahead. The Long Road To Stubs This all started for me with an email discussion in May of 2008, regarding a change request that was filed in 2002, entitled: 4631488 lib/Makefile is too patient: .WAITs should be reduced This CR encapsulates a number of cronic issues with Solaris builds: We build Solaris with a parallel make (dmake) that tries to build as much of the code base in parallel as possible. There is a lot of code to build, and we've long made use of parallelized builds to get the job done quicker. This is even more important in today's world of massively multicore hardware. Solaris contains a large number of executables and shared objects. Executables depend on shared objects, and shared objects can depend on each other. Before you can build an object, you need to ensure that the objects it needs have been built. This implies a need for serialization, which is in direct opposition to the desire to build everying in parallel. To accurately build objects in the right order requires an accurate set of make rules defining the things that depend on each other. This sounds simple, but the reality is quite complex. In practice, having programmers explicitly specify these dependencies is a losing strategy: It's really hard to get right. It's really easy to get it wrong and never know it because things build anyway. Even if you get it right, it won't stay that way, because dependencies between objects can change over time, and make cannot help you detect such drifing. You won't know that you got it wrong until the builds break. That can be a long time after the change that triggered the breakage happened, making it hard to connect the cause and the effect. Usually this happens just before a release, when the pressure is on, its hard to think calmly, and there is no time for deep fixes. As a poor compromise, the libraries in core Solaris were built using a set of grossly incomplete hand written rules, supplemented with a number of dmake .WAIT directives used to group the libraries into sets of non-interacting groups that can be built in parallel because we think they don't depend on each other. From time to time, someone will suggest that we could analyze the built objects themselves to determine their dependencies and then generate make rules based on those relationships. This is possible, but but there are complications that limit the usefulness of that approach: To analyze an object, you have to build it first. This is a classic chicken and egg scenario. You could analyze the results of a previous build, but then you're not necessarily going to get accurate rules for the current code. It should be possible to build the code without having a built workspace available. The analysis will take time, and remember that we're constantly trying to make builds faster, not slower. By definition, such an approach will always be approximate, and therefore only incremantally more accurate than the hand written rules described above. The hand written rules are fast and cheap, while this idea is slow and complex, so we stayed with the hand written approach. Solaris was built that way, essentially forever, because these are genuinely difficult problems that had no easy answer. The makefiles were full of build races in which the right outcomes happened reliably for years until a new machine or a change in build server workload upset the accidental balance of things. After figuring out what had happened, you'd mutter "How did that ever work?", add another incomplete and soon to be inaccurate make dependency rule to the system, and move on. This was not a satisfying solution, as we tend to be perfectionists in the Solaris group, but we didn't have a better answer. It worked well enough, approximately. And so it went for years. We needed a different approach — a new idea to cut the Gordian Knot. In that discussion from May 2008, my fellow linker-alien Rod Evans had the initial spark that lead us to a game changing series of realizations: The link-editor is used to link objects together, but it only uses the ELF metadata in the object, consisting of symbol tables, ELF versioning sections, and similar data. Notably, it does not look at, or understand, the machine code that makes an object useful at runtime. If you had an object that only contained the ELF metadata for a dependency, but not the code or data, the link-editor would find it equally useful for linking, and would never know the difference. Call it a stub object. In the core Solaris OS, we require all objects to be built with a link-editor mapfile that describes all of its publically available functions and data. Could we build a stub object using the mapfile for the real object? It ought to be very fast to build stub objects, as there are no input objects to process. Unlike the real object, stub objects would not actually require any dependencies, and so, all of the stubs for the entire system could be built in parallel. When building the real objects, one could link against the stub objects instead of the real dependencies. This means that all the real objects can be built built in parallel too, without any serialization. We could replace a system that requires perfect makefile rules with a system that requires no ordering rules whatsoever. The results would be considerably more robust. We immediately realized that this idea had potential, but also that there were many details to sort out, lots of work to do, and that perhaps it wouldn't really pan out. As is often the case, it would be necessary to do the work and see how it turned out. Following that conversation, I set about trying to build a stub object. We determined that a faithful stub has to do the following: Present the same set of global symbols, with the same ELF versioning, as the real object. Functions are simple — it suffices to have a symbol of the right type, possibly, but not necessarily, referencing a null function in its text segment. Copy relocations make data more complicated to stub. The possibility of a copy relocation means that when you create a stub, the data symbols must have the actual size of the real data. Any error in this will go uncaught at link time, and will cause tragic failures at runtime that are very hard to diagnose. For reasons too obscure to go into here, involving tentative symbols, it is also important that the data reside in bss, or not, matching its placement in the real object. If the real object has more than one symbol pointing at the same data item, we call these aliased symbols. All data symbols in the stub object must exhibit the same aliasing as the real object. We imagined the stub library feature working as follows: A command line option to ld tells it to produce a stub rather than a real object. In this mode, only mapfiles are examined, and any object or shared libraries on the command line are are ignored. The extra information needed (function or data, size, and bss details) would be added to the mapfile. When building the real object instead of the stub, the extra information for building stubs would be validated against the resulting object to ensure that they match. In exploring these ideas, I immediately run headfirst into the reality of the original mapfile syntax, a subject that I would later write about as The Problem(s) With Solaris SVR4 Link-Editor Mapfiles. The idea of extending that poor language was a non-starter. Until a better mapfile syntax became available, which seemed unlikely in 2008, the solution could not involve extentions to the mapfile syntax. Instead, we cooked up the idea (hack) of augmenting mapfiles with stylized comments that would carry the necessary information. A typical definition might look like: # DATA(i386) __iob 0x3c0 # DATA(amd64,sparcv9) __iob 0xa00 # DATA(sparc) __iob 0x140 iob; A further problem then became clear: If we can't extend the mapfile syntax, then there's no good way to extend ld with an option to produce stub objects, and to validate them against the real objects. The idea of having ld read comments in a mapfile and parse them for content is an unacceptable hack. The entire point of comments is that they are strictly for the human reader, and explicitly ignored by the tool. Taking all of these speed bumps into account, I made a new plan: A perl script reads the mapfiles, generates some small C glue code to produce empty functions and data definitions, compiles and links the stub object from the generated glue code, and then deletes the generated glue code. Another perl script used after both objects have been built, to compare the real and stub objects, using data from elfdump, and validate that they present the same linking interface. By June 2008, I had written the above, and generated a stub object for libc. It was a useful prototype process to go through, and it allowed me to explore the ideas at a deep level. Ultimately though, the result was unsatisfactory as a basis for real product. There were so many issues: The use of stylized comments were fine for a prototype, but not close to professional enough for shipping product. The idea of having to document and support it was a large concern. The ideal solution for stub objects really does involve having the link-editor accept the same arguments used to build the real object, augmented with a single extra command line option. Any other solution, such as our prototype script, will require makefiles to be modified in deeper ways to support building stubs, and so, will raise barriers to converting existing code. A validation script that rederives what the linker knew when it built an object will always be at a disadvantage relative to the actual linker that did the work. A stub object should be identifyable as such. In the prototype, there was no tag or other metadata that would let you know that they weren't real objects. Being able to identify a stub object in this way means that the file command can tell you what it is, and that the runtime linker can refuse to try and run a program that loads one. At that point, we needed to apply this prototype to building Solaris. As you might imagine, the task of modifying all the makefiles in the core Solaris code base in order to do this is a massive task, and not something you'd enter into lightly. The quality of the prototype just wasn't good enough to justify that sort of time commitment, so I tabled the project, putting it on my list of long term things to think about, and moved on to other work. It would sit there for a couple of years. Semi-coincidentally, one of the projects I tacked after that was to create a new mapfile syntax for the Solaris link-editor. We had wanted to do something about the old mapfile syntax for many years. Others before me had done some paper designs, and a great deal of thought had already gone into the features it should, and should not have, but for various reasons things had never moved beyond the idea stage. When I joined Sun in late 2005, I got involved in reviewing those things and thinking about the problem. Now in 2008, fresh from relearning for the Nth time why the old mapfile syntax was a huge impediment to linker progress, it seemed like the right time to tackle the mapfile issue. Paving the way for proper stub object support was not the driving force behind that effort, but I certainly had them in mind as I moved forward. The new mapfile syntax, which we call version 2, integrated into Nevada build snv_135 in in February 2010: 6916788 ld version 2 mapfile syntax PSARC/2009/688 Human readable and extensible ld mapfile syntax In order to prove that the new mapfile syntax was adequate for general purpose use, I had also done an overhaul of the ON consolidation to convert all mapfiles to use the new syntax, and put checks in place that would ensure that no use of the old syntax would creep back in. That work went back into snv_144 in June 2010: 6916796 OSnet mapfiles should use version 2 link-editor syntax That was a big putback, modifying 517 files, adding 18 new files, and removing 110 old ones. I would have done this putback anyway, as the work was already done, and the benefits of human readable syntax are obvious. However, among the justifications listed in CR 6916796 was this We anticipate adding additional features to the new mapfile language that will be applicable to ON, and which will require all sharable object mapfiles to use the new syntax. I never explained what those additional features were, and no one asked. It was premature to say so, but this was a reference to stub objects. By that point, I had already put together a working prototype link-editor with the necessary support for stub objects. I was pleased to find that building stubs was indeed very fast. On my desktop system (Ultra 24), an amd64 stub for libc can can be built in a fraction of a second: % ptime ld -64 -z stub -o stubs/libc.so.1 -G -hlibc.so.1 \ -ztext -zdefs -Bdirect ... real 0.019708910 user 0.010101680 sys 0.008528431 In order to go from prototype to integrated link-editor feature, I knew that I would need to prove that stub objects were valuable. And to do that, I knew that I'd have to switch the Solaris ON consolidation to use stub objects and evaluate the outcome. And in order to do that experiment, ON would first need to be converted to version 2 mapfiles. Sub-mission accomplished. Normally when you design a new feature, you can devise reasonably small tests to show it works, and then deploy it incrementally, letting it prove its value as it goes. The entire point of stub objects however was to demonstrate that they could be successfully applied to an extremely large and complex code base, and specifically to solve the Solaris build issues detailed above. There was no way to finesse the matter — in order to move ahead, I would have to successfully use stub objects to build the entire ON consolidation and demonstrate their value. In software, the need to boil the ocean can often be a warning sign that things are trending in the wrong direction. Conversely, sometimes progress demands that you build something large and new all at once. A big win, or a big loss — sometimes all you can do is try it and see what happens. And so, I spent some time staring at ON makefiles trying to get a handle on how things work, and how they'd have to change. It's a big and messy world, full of complex interactions, unspecified dependencies, special cases, and knowledge of arcane makefile features... ...and so, I backed away, put it down for a few months and did other work... ...until the fall, when I felt like it was time to stop thinking and pondering (some would say stalling) and get on with it. Without stubs, the following gives a simplified high level view of how Solaris is built: An initially empty directory known as the proto, and referenced via the ROOT makefile macro is established to receive the files that make up the Solaris distribution. A top level setup rule creates the proto area, and performs operations needed to initialize the workspace so that the main build operations can be launched, such as copying needed header files into the proto area. Parallel builds are launched to build the kernel (usr/src/uts), libraries (usr/src/lib), and commands. The install makefile target builds each item and delivers a copy to the proto area. All libraries and executables link against the objects previously installed in the proto, implying the need to synchronize the order in which things are built. Subsequent passes run lint, and do packaging. Given this structure, the additions to use stub objects are: A new second proto area is established, known as the stub proto and referenced via the STUBROOT makefile macro. The stub proto has the same structure as the real proto, but is used to hold stub objects. All files in the real proto are delivered as part of the Solaris product. In contrast, the stub proto is used to build the product, and then thrown away. A new target is added to library Makefiles called stub. This rule builds the stub objects. The ld command is designed so that you can build a stub object using the same ld command line you'd use to build the real object, with the addition of a single -z stub option. This means that the makefile rules for building the stub objects are very similar to those used to build the real objects, and many existing makefile definitions can be shared between them. A new target is added to the Makefiles called stubinstall which delivers the stub objects built by the stub rule into the stub proto. These rules reuse much of existing plumbing used by the existing install rule. The setup rule runs stubinstall over the entire lib subtree as part of its initialization. All libraries and executables link against the objects in the stub proto rather than the main proto, and can therefore be built in parallel without any synchronization. There was no small way to try this that would yield meaningful results. I would have to take a leap of faith and edit approximately 1850 makefiles and 300 mapfiles first, trusting that it would all work out. Once the editing was done, I'd type make and see what happened. This took about 6 weeks to do, and there were many dark days when I'd question the entire project, or struggle to understand some of the many twisted and complex situations I'd uncover in the makefiles. I even found a couple of new issues that required changes to the new stub object related code I'd added to ld. With a substantial amount of encouragement and help from some key people in the Solaris group, I eventually got the editing done and stub objects for the entire workspace built. I found that my desktop system could build all the stub objects in the workspace in roughly a minute. This was great news, as it meant that use of the feature is effectively free — no one was likely to notice or care about the cost of building them. After another week of typing make, fixing whatever failed, and doing it again, I succeeded in getting a complete build! The next step was to remove all of the make rules and .WAIT statements dedicated to controlling the order in which libraries under usr/src/lib are built. This came together pretty quickly, and after a few more speed bumps, I had a workspace that built cleanly and looked like something you might actually be able to integrate someday. This was a significant milestone, but there was still much left to do. I turned to doing full nightly builds. Every type of build (open, closed, OpenSolaris, export, domestic) had to be tried. Each type failed in a new and unique way, requiring some thinking and rework. As things came together, I became aware of things that could have been done better, simpler, or cleaner, and those things also required some rethinking, the seeking of wisdom from others, and some rework. After another couple of weeks, it was in close to final form. My focus turned towards the end game and integration. This was a huge workspace, and needed to go back soon, before changes in the gate would made merging increasingly difficult. At this point, I knew that the stub objects had greatly simplified the makefile logic and uncovered a number of race conditions, some of which had been there for years. I assumed that the builds were faster too, so I did some builds intended to quantify the speedup in build time that resulted from this approach. It had never occurred to me that there might not be one. And so, I was very surprised to find that the wall clock build times for a stock ON workspace were essentially identical to the times for my stub library enabled version! This is why it is important to always measure, and not just to assume. One can tell from first principles, based on all those removed dependency rules in the library makefile, that the stub object version of ON gives dmake considerably more opportunities to overlap library construction. Some hypothesis were proposed, and shot down: Could we have disabled dmakes parallel feature? No, a quick check showed things being build in parallel. It was suggested that we might be I/O bound, and so, the threads would be mostly idle. That's a plausible explanation, but system stats didn't really support it. Plus, the timing between the stub and non-stub cases were just too suspiciously identical. Are our machines already handling as much parallelism as they are capable of, and unable to exploit these additional opportunities? Once again, we didn't see the evidence to back this up. Eventually, a more plausible and obvious reason emerged: We build the libraries and commands (usr/src/lib, usr/src/cmd) in parallel with the kernel (usr/src/uts). The kernel is the long leg in that race, and so, wall clock measurements of build time are essentially showing how long it takes to build uts. Although it would have been nice to post a huge speedup immediately, we can take solace in knowing that stub objects simplify the makefiles and reduce the possibility of race conditions. The next step in reducing build time should be to find ways to reduce or overlap the uts part of the builds. When that leg of the build becomes shorter, then the increased parallelism in the libs and commands will pay additional dividends. Until then, we'll just have to settle for simpler and more robust. And so, I integrated the link-editor support for creating stub objects into snv_153 (November 2010) with 6993877 ld should produce stub objects PSARC/2010/397 ELF Stub Objects followed by the work to convert the ON consolidation in snv_161 (February 2011) with 7009826 OSnet should use stub objects 4631488 lib/Makefile is too patient: .WAITs should be reduced This was a huge putback, with 2108 modified files, 8 new files, and 2 removed files. Due to the size, I was allowed a window after snv_160 closed in which to do the putback. It went pretty smoothly for something this big, a few more preexisting race conditions would be discovered and addressed over the next few weeks, and things have been quiet since then. Conclusions and Looking Forward Solaris has been built with stub objects since February. The fact that developers no longer specify the order in which libraries are built has been a big success, and we've eliminated an entire class of build error. That's not to say that there are no build races left in the ON makefiles, but we've taken a substantial bite out of the problem while generally simplifying and improving things. The introduction of a stub proto area has also opened some interesting new possibilities for other build improvements. As this article has become quite long, and as those uses do not involve stub objects, I will defer that discussion to a future article.

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  • JavaScript distributed computing project

    - by Ben L.
    I made a website that does absolutely nothing, and I've proven to myself that people like to stay there - I've already logged 11+ hours worth of cumulative time on the page. My question is whether it would be possible (or practical) to use the website as a distributed computing site. My first impulse was to find out if there were any JavaScript distributed computing projects already active, so that I could put a piece of code on the page and be done. Unfortunately, all I could find was a big list of websites that thought it might be a cool idea. I'm thinking that I might want to start with something like integer factorization - in this case, RSA numbers. It would be easy for the server to check if an answer was correct (simply test for modulus equals zero), and also easy to implement. Is my idea feasible? Is there already a project out there that I can use?

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  • Synchronizing local and remote cache in distributed caching

    - by ltfishie
    With a distributed cache, a subset of the cache is kept locally while the rest is held remotely. In a get operation, if the entry is not available locally, the remote cache will be used and and the entry is added to local cache. In a put operation, both the local cache and remote cache are updated. Other nodes in the cluster also need to be notified to invalidate their local cache as well. What's a simplest way to achieve this if you implemented it yourself, assuming that nodes are not aware of each other. Edit My current implementation goes like this: Each cache entry contains a time stamp. Put operation will update local cache and remote cache Get operation will try local cache then remote cache A background thread on each node will check remote cache periodically for each entry in local cache. If the timestamp on remote is newer overwrite the local. If entry is not found in remote, delete it from local.

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  • Scalable distributed file system for blobs like images and other documents

    - by Pinnacle
    Cassandra & HBase both do not efficiently support storage of blobs like images. Storing directly on HDFS stresses the Namenode because of huge number of files. Facebook uses Haystack for images and attachments storage, but this is not open source. So is Lustre a good choice for distributed blob storage? I have read that Amazon S3 is used by many, but this would cost money and personally, I would not like to rely on third party system. What are other suggestions?

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  • Creating Multiple Queries for Running Objects

    - by edurdias
    Running Objects combines the power of LINQ with Metadata definition to let you leverage multiples perspectives of your queries of objects. By default, RO brings all the objects in natural order of insertion and including all the visible properties of your class. In this post, we will understand how the QueryAttribute class is structured and how to make use of it. The QueryAttribute class This class is the responsible to specify all the possible perspectives of a list of objects. In other words, is...(read more)

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  • Distributed transactions

    - by javi
    Hello! I've a question regarding distributed transactions. Let's assume I have 3 transaction programs: Transaction A begin a=read(A) b=read(B) c=a+b write(C,c) commit Transaction B begin a=read(A) a=a+1 write(A,a) commit Transaction C begin c=read(C) c=c*2 write(A,c) commit So there are 5 pairs of critical operations: C2-A5, A2-B4, B4-C4, B2-C4, A2-C4. I should ensure integrity and confidentiality, do you have any idea of how to achieve it? Thank you in advance!

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  • Optimistic work sharing on sparsely distributed systems

    - by Asti
    What would a system like BOINC look like if it were written today? At the time BOINC was written, databases were the primary choice for maintaining a shared state and concurrency among nodes. Since then, many approaches have been developed for tasking with optimistic concurrency (OT, partial synchronization primitives, shared iterators etc.) Is there an optimal paradigm for optimistically distributing units of work on sparsely distributing systems which communicate through message passing? Sorry if this is a bit vague. P.S. The concept of Tuple-spaces is great, but locking is inherent to its definition. Edit: I already have a federation system which works very well. I have a reactive OT system is implemented on top of it. I'm looking to extend it to get clients to do units of work.

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  • Distributed Transaction Framework across webservices

    - by John Petrak
    I am designing a new system that has one central web service and several site web services which are spread across the country and some overseas. It has some data that must be identical on all sites. So my plan is to maintain that data in the central web service and then "sync" the data to sites. This includes inserts, edits and deletes. I see a problem when deleting, if one site has used the record, then I need to undo the delete that has happened on the other servers. This lead me to idea that I need some sort of transaction system that can work across different web servers. Before I design one from scratch, I would like to know if anyone has come across this sort of problem and if there are any frame works or even design patterns that might aid me?

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  • Interconnect nodes in a Java distributed infrastructure for tweet processing

    - by David Moreno García
    I'm working in a new version of an old project that I used to download and process user statuses from Twitter. The main problem of that project was its infrastructure. I used multiple instances of a java application (trackers) to download from Twitter given an specific task (basically terms to search for), connected with a central node (a web application) that had to process all tweets once per day and generate a new task for each trackers once each 15 minutes. The central node also had to monitor all trackers and enable/disable them under user petition. This, as I said, was too slow because I had multiple bottlenecks, so in this new version I want to improve the infrastructure and isolate all functionalities in specific nodes. I also need a good notification system to receive notifications for any node. So, in the next diagram I show the components that I'll need in this new version: As you can see, there are more nodes. Here are some notes about them: Dashboard: Controls trackers statuses and send a single task to each of them (under user request). The trackers will use this task until replaced with a new one (if done, not each 15 minutes like before). Search engine: I need to store all the tweets. They are firstly stored in a local database for each tracker but after that I'm thinking on using something like Elasticsearch to be able to do fast searches. Tweet processor: Just and isolated component with its own database (maybe something like the search engine to have fast access to info generated by the module). In the future more could be added. Application UI: A web application with a shared database with the Dashboard (mainly to store users information and preferences). Indeed, both could be merged into a single web. The main difference with the previous version of the project is that now they will be isolated and they will only show information and send requests. I will not do any heavy task in them (like process tweets as I did before). So, having this components, my main headache is how to structure all to not have to rewrite a lot of code every time I need to access any new data. Another headache is how can I interconnect nodes. I could use sockets but that is a pain in the ass. Maybe a REST layer? And finally, if all the nodes are isolated, how could I generate notifications for each user which info is only in the database used by the Application UI? I'm programming this using Java and Spring (at least I used them in the last version) but I have no problems with changing the language if I can take advantage of a tool/library/engine to make my life easier and have a better platform. Any comment will be appreciated.

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  • I've totally missed the point of distributed vcs [closed]

    - by NimChimpsky
    I thought the major benefit of it was that each developers code gets stored within each others repository. My impression was that each developer has their working directory, their own repository, and then a copy of the other developers repository. Removing the need for central server, as you have as many backups as you have developers/repositories Turns out this is nto the case, and your code is only backed up (somewhere other than locally) when you push, the same as a commit in subversions. I am bit disappointed ... hopefully I will be pleasantly surprised when it handles merges better and there are less conflicts ?

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  • Building a Redundant / Distrubuted Application

    - by MattW
    This is more of a "point me in the right direction" question. I (and my team of 3) have built a hosted web app that queues and routes customer chat requests to available customer service agents (It does other things as well, but this is enough background to illustrate the issue). The basic dev architecture today is: a single page ajax web UI (ASP.NET MVC) with floating chat windows (think Gmail) a backend Windows service to queue and route the chat requests this service also logs the chats, calculates service levels, etc a Comet server product that routes data between the web frontend and the backend Windows service this also helps us detect which Agents are still connected (online) And our hardware architecture today is: 2 servers to host the web UI portion of the application a load balancer to route requests to the 2 different web app servers a third server to host the SQL Server DB and the backend Windows service responsible for queuing / delivering chats So as it stands today, one of the web app servers could go down and we would be ok. However, if something would happen to the SQL Server / Windows Service server we would be boned. My question - how can I make this backend Windows service logic be able to be spread across multiple machines (distributed)? The Windows service is written to accept requests from the Comet server, check for available Agents, and route the chat to those agents. How can I make this more distributed? How can I make it so that I can distribute the work of the backend Windows service can be spread across multiple machines for redundancy and uptime purposes? Will I need to re-write it with distributed computing in mind? I should also note that I am hosting all of this on Rackspace Cloud instances - so maybe it is something I should be less concerned about? Thanks in advance for any help!

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