Ramcloud Reloaded: Log-Structured Retentivity For Dram-Based Storage
I had written a review close "the illustration for RAMCloud" newspaper inwards 2010. RAMCloud advocates storing all the information inwards the RAM over distributed nodes inwards a datacenter. A RAMCloud is non a cache similar memcached as well as information is non stored on an I/O device; DRAM is the permanent domicile for data. Obviously, storing everything inwards RAM could yield a really high-throughput (100-1000x) as well as really low-latency (100-1000x) scheme compared to disk-based systems.
In the in conclusion 3 years, the RAMCloud group headed yesteryear John Ousterhout at Stanford has done meaning function on this project, as well as this is a expert fourth dimension to write approximately other review on RAMCloud. My review generally uses (shortened) text shape their papers, as well as focuses on approximately of the meaning ideas inwards their work.
Data model. RAMCloud provides a elementary key-value information model consisting of uninterpreted information blobs called objects. Objects are grouped into tables that may bridge i or to a greater extent than servers inwards the cluster; a subset of a tabular array stored on a unmarried server is called a tablet. Objects must move read or written inwards their entirety. RAMCloud is optimized for small-scale objects --a few hundred bytes or less-- but supports objects upwards to 1 MB.
Each master's retentiveness contains a hash tabular array as well as a collection of objects stored inwards DRAM. The hash tabular array contains i entry for each object stored on that master; it allows whatsoever object to move located quickly, given its tabular array as well as key.
RAMCloud recovery. To ensure information durability against crashes as well as might failures, each original must cash inwards one's chips along backup copies of its objects on the secondary storage of other servers. The backup information is organized as a log for maximum efficiency. Each original has its ain log, which is divided into 8 MB pieces called segments. Each segment is replicated on several backups (typically 2 or three). A original uses a dissimilar laid of backups to replicate each segment, thus that its segment replicas destination upwards scattered across the entire cluster. E.g., this segment replicated at nodes xi as well as 44, as well as the side yesteryear side segment at nodes 26 as well as 37.
When a original receives a write asking from a client, it adds the novel object to its retentiveness as well as forwards information close that object to the backups. The backups append the novel object to segment replicas stored inwards nonvolatile buffers; they answer to the original equally before long equally the object has been copied into their buffer, without issuing an I/O to secondary storage. Once the original has received replies from all the backups, it responds to the client. Each backup accumulates information inwards its buffer until the segment is complete, at which signal it writes the segment to secondary storage as well as reallocates the buffer for approximately other segment.
RAMCloud recovery is thus possible yesteryear reading from the segments as well as constructing the hashtables at the masters. The newspaper describing this recovery process appeared inwards SOSP 2011.
RAMCloud log cleaner. RAMCloud uses a log cleaner to reclaim gratis infinite that accumulates inwards the logs when objects are deleted or over-written. Each original runs a separate cleaner. The cleaner selects several segments to create clean (based on the amount of gratis infinite & historic menstruum of data). The cleaner thus scans these segments stored inwards retentiveness as well as copies whatsoever move objects to novel survivor segments. Liveness is determined yesteryear checking for a reference to the object inwards the hash table. The cleaner makes the one-time segments' retentiveness available for novel segments, as well as notifies the backups for those segments that they tin dismiss reclaim the storage for the replicas.
In this post, nosotros volition hold off at the newspaper for the RAMCloud log cleaner, which has been published equally a technical report as well as is nether submission at SOSP'13.
GC comes alongside a trade-off: at approximately signal all of these collectors (even those that label themselves equally "incremental") must walk all move data, relocate it, as well as update references. This is an expensive functioning that scales poorly, thus GC delay global collections until a large amount of garbage has accumulated. As a result, they typically require 1.5-5x equally much infinite equally is genuinely used inwards guild to keep high performance. This erases whatsoever infinite savings gained yesteryear defragmenting memory.
Pause times are approximately other employment organisation alongside copying allocators alongside GC. At approximately signal all collectors must stop the processes' threads to update references when objects are moved. Despite function on real-time garbage collectors, fifty-fifty state-of-art solutions have got maximum suspension times of hundreds of microseconds, or fifty-fifty milliseconds --this is 100 to 1,000 times longer than the round-trip fourth dimension for a RAMCloud RPC.
In RAMCloud it was a natural selection to role a logging approach on disk to backup the information stored inwards original retentiveness (given likewise that log-structured file-system (LSF) has been introduced yesteryear Ousterhout inwards 1991, it was inevitable genuinely :-). However, it was surprising to respect that logging likewise makes feel equally a technique for managing the information inwards DRAM: Log-structured retentiveness (LSM) takes wages of /the restricted role of pointers/ inwards storage systems /to eliminate the global retentiveness scans/ that fundamentally bound existing garbage collectors. The resultant is an efficient as well as highly incremental shape of copying garbage collector that allows retentiveness to move used efficiently fifty-fifty at utilizations of 80-90%.
RAMCloud uses a unmarried log-based approach for managing both disk as well as original memory, alongside small-scale policy differences that optimize the usage of each medium. Combining LSM as well as LSF, RAMCloud adopts a 2-level approach to cleaning alongside dissimilar policies for cleaning information inwards retentiveness versus secondary storage. Since log information is immutable i time appended, the log cleaner tin dismiss run concurrently alongside normal read as well as write operations. Furthermore, multiple cleaners tin dismiss run inwards separate threads. In the residual of this post, nosotros volition hash out each of these ideas: the LSM logs, 2-level cleaning, as well as parallel cleaning.
To reclaim gratis space, the log cleaner should re-create move information out of the segments it chooses for cleaning. Unfortunately, the toll of log cleaning at the disk rises rapidly equally retentiveness utilization approaches 100%. E.g., if segments are cleaned when 80% of their information are soundless live, the cleaner must re-create iv bytes of move information for every byte it frees. If segments are cleaned at 90% utilization, the cleaner must re-create nine bytes of move information for every byte it frees.
The starting fourth dimension degree of cleaning, called segment compaction, operates exclusively on the in-memory segments on masters. It compacts a unmarried segment at a time, copying its move information into a smaller part of retentiveness as well as freeing the original storage for novel segments. Segment crunch maintains the same logical log inwards retentiveness as well as on disk: each segment inwards retentiveness soundless has a corresponding segment on disk. However, the segment inwards retentiveness takes less infinite because deleted objects as well as obsolete tombstones have got been removed.
The mo degree of cleaning is called combined cleaning. If the disk log is allowed to grow until it consumes twice equally much infinite equally the log inwards memory, the utilization of segments cleaned on disk volition never move greater than 50%, which makes cleaning relatively efficient.
Segments as well as seglets. With compaction, however, segments inwards retentiveness tin dismiss have got dissimilar sizes. Each RAMCloud original divides its retentiveness into fixed-size 64KB seglets. A segment consists of a collection of seglets, as well as the issue of seglets varies alongside the size of the segment. Segment crunch cannot reorganize data, since it must save the 1-to-1 mapping betwixt segments inwards retentiveness as well as those on disk. Combined cleaning is at that topographic point to enable segment reorganization.
There are 3 points of disputation betwixt cleaner threads as well as service threads treatment read as well as write requests: 1) both cleaner as well as service threads take to add together information at the caput of the log, 2) the threads may conflict inwards updates to the hash table, 3) the cleaner must non gratis segments that are soundless inwards role yesteryear service threads. We consider these 3 disputation points next.
Head of log contention. The most obvious way to perform cleaning is to re-create the move information to the caput of the log, but this would create disputation for the log caput betwixt cleaner threads as well as service threads that are writing novel data. RAMCloud's solution is for the cleaner to write survivor information to dissimilar segments than the log head. Each cleaner thread allocates a separate laid of segments for its survivor data. Synchronization is required when allocating segments, but i time segments are allocated, each cleaner thread tin dismiss re-create information to its ain survivor segments without additional synchronization.
Hash tabular array contention. Hash tabular array is used both yesteryear service threads as well as cleaner threads. The cleaner uses the hash tabular array to depository fiscal establishment check whether an object is move (by seeing if the hash tabular array currently points to that exact object). If the object is alive, the cleaner copies it as well as updates the hash tabular array to hollo to the novel place inwards a survivor segment. To ensure consistency spell reducing contention, RAMCloud currently uses fine-grained locks on private hash tabular array buckets. Although disputation for these locks is depression (only 0.1% nether heavy write as well as cleaner load) it soundless agency that the cleaner must larn as well as release a lock for every object it scans, as well as read as well as write requests must likewise larn an extra lock. Lockless solution is hereafter work.
Freeing segments inwards memory. Once a cleaner thread has cleaned a segment, the segment's storage inwards retentiveness tin dismiss move freed for reuse. However, it is possible that a service thread had begun using the information inwards the segment earlier the cleaner updated the hash table; if so, the cleaner must non gratis the segment until the service thread has finished using it. A elementary solution is to role a reader-writer lock for each segment, alongside service threads asset reader locks spell using a segment as well as cleaner threads asset author locks earlier freeing a segment. However, this would growth the latency of all read requests yesteryear adding approximately other lock inwards the critical path.
Instead, RAMCloud uses a machinery based on epochs, which avoids locking inwards service threads. The exclusively additional overhead for service threads is to read a global epoch variable as well as shop it alongside the RPC. When a cleaner thread finishes a cleaning pass, it increments the epoch as well as thus tags each cleaned segment alongside the novel epoch (after this point, no novel asking volition role the cleaned segment). The cleaner occasionally scans the epochs of active RPCs as well as frees the segment when all RPCs alongside epochs less than the segment's epoch have got completed. This approach creates additional overhead for segment freeing, but these operations are infrequent as well as run inwards a separate thread where they don't touching on read as well as write times.
To recap, this function showed that logging likewise makes feel equally a technique for managing the information inwards DRAM: Log-structured retentiveness takes wages of /the restricted role of pointers/ inwards storage systems /to eliminate the global retentiveness scans/ that fundamentally bound existing garbage collectors. The resultant is an efficient as well as highly incremental shape of copying garbage collector that allows retentiveness to move used efficiently fifty-fifty at utilizations of 80-90%.
Questions for farther thinking along these lines:
"Each object inwards the log must move self-identifying; when the log is scanned during crash recovery, this information allows RAMCloud to position the most recent version of an object as well as reconstruct the hash table." Then, why non backup (copy reconstruct) the hash-table equally well?
"Log-structured retentiveness takes wages of the restricted role of pointers inwards storage systems to eliminate the global retentiveness scans that fundamentally bound existing garbage collectors." Can you lot hollo upwards of similar "restricted role of pointers" approaches to enable log crunch as well as parallel cleaning?
LSM worked for storage systems. What other applications/use-cases would this move appropriate? What applications/use-cases would this move inappropriate?
In the in conclusion 3 years, the RAMCloud group headed yesteryear John Ousterhout at Stanford has done meaning function on this project, as well as this is a expert fourth dimension to write approximately other review on RAMCloud. My review generally uses (shortened) text shape their papers, as well as focuses on approximately of the meaning ideas inwards their work.
State of the RAMCloud
Data model. RAMCloud provides a elementary key-value information model consisting of uninterpreted information blobs called objects. Objects are grouped into tables that may bridge i or to a greater extent than servers inwards the cluster; a subset of a tabular array stored on a unmarried server is called a tablet. Objects must move read or written inwards their entirety. RAMCloud is optimized for small-scale objects --a few hundred bytes or less-- but supports objects upwards to 1 MB.
Each master's retentiveness contains a hash tabular array as well as a collection of objects stored inwards DRAM. The hash tabular array contains i entry for each object stored on that master; it allows whatsoever object to move located quickly, given its tabular array as well as key.
RAMCloud recovery. To ensure information durability against crashes as well as might failures, each original must cash inwards one's chips along backup copies of its objects on the secondary storage of other servers. The backup information is organized as a log for maximum efficiency. Each original has its ain log, which is divided into 8 MB pieces called segments. Each segment is replicated on several backups (typically 2 or three). A original uses a dissimilar laid of backups to replicate each segment, thus that its segment replicas destination upwards scattered across the entire cluster. E.g., this segment replicated at nodes xi as well as 44, as well as the side yesteryear side segment at nodes 26 as well as 37.
When a original receives a write asking from a client, it adds the novel object to its retentiveness as well as forwards information close that object to the backups. The backups append the novel object to segment replicas stored inwards nonvolatile buffers; they answer to the original equally before long equally the object has been copied into their buffer, without issuing an I/O to secondary storage. Once the original has received replies from all the backups, it responds to the client. Each backup accumulates information inwards its buffer until the segment is complete, at which signal it writes the segment to secondary storage as well as reallocates the buffer for approximately other segment.
RAMCloud recovery is thus possible yesteryear reading from the segments as well as constructing the hashtables at the masters. The newspaper describing this recovery process appeared inwards SOSP 2011.
RAMCloud log cleaner. RAMCloud uses a log cleaner to reclaim gratis infinite that accumulates inwards the logs when objects are deleted or over-written. Each original runs a separate cleaner. The cleaner selects several segments to create clean (based on the amount of gratis infinite & historic menstruum of data). The cleaner thus scans these segments stored inwards retentiveness as well as copies whatsoever move objects to novel survivor segments. Liveness is determined yesteryear checking for a reference to the object inwards the hash table. The cleaner makes the one-time segments' retentiveness available for novel segments, as well as notifies the backups for those segments that they tin dismiss reclaim the storage for the replicas.
In this post, nosotros volition hold off at the newspaper for the RAMCloud log cleaner, which has been published equally a technical report as well as is nether submission at SOSP'13.
The employment alongside Memory allotment & Garbage collection
Memory allocators autumn into 2 full general classes: noncopying allocators as well as copying allocators. Non-copying allocators such equally malloc cannot motion an object i time it has been allocated, thus they are vulnerable to fragmentation. Non-copying allocators function good for private applications alongside a consistent distribution of object sizes, but they tin dismiss easily waste matter one-half of retentiveness when allotment patterns change. Copying allocators are those that tin dismiss motion objects afterward they have got been created. In principle, garbage collecting (GC) tin dismiss solve the fragmentation employment yesteryear moving move information to coalesce gratis heap space.GC comes alongside a trade-off: at approximately signal all of these collectors (even those that label themselves equally "incremental") must walk all move data, relocate it, as well as update references. This is an expensive functioning that scales poorly, thus GC delay global collections until a large amount of garbage has accumulated. As a result, they typically require 1.5-5x equally much infinite equally is genuinely used inwards guild to keep high performance. This erases whatsoever infinite savings gained yesteryear defragmenting memory.
Pause times are approximately other employment organisation alongside copying allocators alongside GC. At approximately signal all collectors must stop the processes' threads to update references when objects are moved. Despite function on real-time garbage collectors, fifty-fifty state-of-art solutions have got maximum suspension times of hundreds of microseconds, or fifty-fifty milliseconds --this is 100 to 1,000 times longer than the round-trip fourth dimension for a RAMCloud RPC.
Log Structured Memory (LSM)
Existing allocators are non able to role retentiveness efficiently, specially inwards the confront of changing access patterns, thus are non applicable for RAMCloud. An ideal retentiveness allocator for a DRAM-based storage scheme such equally RAMCloud should have got 2 properties: 1) It must move able to re-create objects inwards guild to eliminate fragmentation. 2) It must non require a global scan of memory: instead, it must move able to perform the copying incrementally, garbage collecting small-scale regions of retentiveness independently alongside toll proportional to the size of a region. This paper shows how to role a log-structured approach to retentiveness management to orbit fragmentation-free as well as fast retentiveness allocation.In RAMCloud it was a natural selection to role a logging approach on disk to backup the information stored inwards original retentiveness (given likewise that log-structured file-system (LSF) has been introduced yesteryear Ousterhout inwards 1991, it was inevitable genuinely :-). However, it was surprising to respect that logging likewise makes feel equally a technique for managing the information inwards DRAM: Log-structured retentiveness (LSM) takes wages of /the restricted role of pointers/ inwards storage systems /to eliminate the global retentiveness scans/ that fundamentally bound existing garbage collectors. The resultant is an efficient as well as highly incremental shape of copying garbage collector that allows retentiveness to move used efficiently fifty-fifty at utilizations of 80-90%.
RAMCloud uses a unmarried log-based approach for managing both disk as well as original memory, alongside small-scale policy differences that optimize the usage of each medium. Combining LSM as well as LSF, RAMCloud adopts a 2-level approach to cleaning alongside dissimilar policies for cleaning information inwards retentiveness versus secondary storage. Since log information is immutable i time appended, the log cleaner tin dismiss run concurrently alongside normal read as well as write operations. Furthermore, multiple cleaners tin dismiss run inwards separate threads. In the residual of this post, nosotros volition hash out each of these ideas: the LSM logs, 2-level cleaning, as well as parallel cleaning.
Log metadata as well as log cleaning
Each novel log segment contains a log digest that describes the entire log. Every segment has a unique identifier, allocated inwards ascending guild inside a log (see Fig 3 above). Each object inwards the log must move self-identifying: it contains the tabular array identifier, key, as well as version issue for the object inwards add-on to its value. Log metadata likewise contains tombstones that position deleted objects. When an object is deleted or modified, RAMCloud does non modify the object's existing tape inwards the log. Instead, it appends a tombstone tape to the log.To reclaim gratis space, the log cleaner should re-create move information out of the segments it chooses for cleaning. Unfortunately, the toll of log cleaning at the disk rises rapidly equally retentiveness utilization approaches 100%. E.g., if segments are cleaned when 80% of their information are soundless live, the cleaner must re-create iv bytes of move information for every byte it frees. If segments are cleaned at 90% utilization, the cleaner must re-create nine bytes of move information for every byte it frees.
2-Level cleaning
In the original implementation of RAMCloud, disk as well as retentiveness cleaning were tied together: cleaning inwards retentiveness was mirrored to the backup copies on disk. This made it impossible to orbit both high retentiveness utilization as well as high write throughput. The solution to this is to create clean in-memory as well as on-disk logs independently: 2-level cleaning. This way, retentiveness tin dismiss have got higher utilization than disk. The cleaning toll for retentiveness volition move high, but DRAM tin dismiss easily supply the bandwidth required to create clean at 90% utilization or higher. Disk cleaning happens less often. The disk log becomes larger than the in-memory log, thus it has lower overall utilization (50%), as well as this reduces the bandwidth required for cleaning.The starting fourth dimension degree of cleaning, called segment compaction, operates exclusively on the in-memory segments on masters. It compacts a unmarried segment at a time, copying its move information into a smaller part of retentiveness as well as freeing the original storage for novel segments. Segment crunch maintains the same logical log inwards retentiveness as well as on disk: each segment inwards retentiveness soundless has a corresponding segment on disk. However, the segment inwards retentiveness takes less infinite because deleted objects as well as obsolete tombstones have got been removed.
The mo degree of cleaning is called combined cleaning. If the disk log is allowed to grow until it consumes twice equally much infinite equally the log inwards memory, the utilization of segments cleaned on disk volition never move greater than 50%, which makes cleaning relatively efficient.
Segments as well as seglets. With compaction, however, segments inwards retentiveness tin dismiss have got dissimilar sizes. Each RAMCloud original divides its retentiveness into fixed-size 64KB seglets. A segment consists of a collection of seglets, as well as the issue of seglets varies alongside the size of the segment. Segment crunch cannot reorganize data, since it must save the 1-to-1 mapping betwixt segments inwards retentiveness as well as those on disk. Combined cleaning is at that topographic point to enable segment reorganization.
Parallel cleaning
Parallel cleaning inwards RAMCloud is greatly simplified yesteryear the role of a log construction as well as elementary metadata: Since segments are immutable afterward they are created, the cleaner never needs to worry close objects beingness modified spell the cleaner is copying them. This agency that the basic cleaning machinery is really straightforward: the cleaner copies move information to novel segments, atomically updates references inwards the hash table, as well as frees the cleaned segments.There are 3 points of disputation betwixt cleaner threads as well as service threads treatment read as well as write requests: 1) both cleaner as well as service threads take to add together information at the caput of the log, 2) the threads may conflict inwards updates to the hash table, 3) the cleaner must non gratis segments that are soundless inwards role yesteryear service threads. We consider these 3 disputation points next.
Head of log contention. The most obvious way to perform cleaning is to re-create the move information to the caput of the log, but this would create disputation for the log caput betwixt cleaner threads as well as service threads that are writing novel data. RAMCloud's solution is for the cleaner to write survivor information to dissimilar segments than the log head. Each cleaner thread allocates a separate laid of segments for its survivor data. Synchronization is required when allocating segments, but i time segments are allocated, each cleaner thread tin dismiss re-create information to its ain survivor segments without additional synchronization.
Hash tabular array contention. Hash tabular array is used both yesteryear service threads as well as cleaner threads. The cleaner uses the hash tabular array to depository fiscal establishment check whether an object is move (by seeing if the hash tabular array currently points to that exact object). If the object is alive, the cleaner copies it as well as updates the hash tabular array to hollo to the novel place inwards a survivor segment. To ensure consistency spell reducing contention, RAMCloud currently uses fine-grained locks on private hash tabular array buckets. Although disputation for these locks is depression (only 0.1% nether heavy write as well as cleaner load) it soundless agency that the cleaner must larn as well as release a lock for every object it scans, as well as read as well as write requests must likewise larn an extra lock. Lockless solution is hereafter work.
Freeing segments inwards memory. Once a cleaner thread has cleaned a segment, the segment's storage inwards retentiveness tin dismiss move freed for reuse. However, it is possible that a service thread had begun using the information inwards the segment earlier the cleaner updated the hash table; if so, the cleaner must non gratis the segment until the service thread has finished using it. A elementary solution is to role a reader-writer lock for each segment, alongside service threads asset reader locks spell using a segment as well as cleaner threads asset author locks earlier freeing a segment. However, this would growth the latency of all read requests yesteryear adding approximately other lock inwards the critical path.
Instead, RAMCloud uses a machinery based on epochs, which avoids locking inwards service threads. The exclusively additional overhead for service threads is to read a global epoch variable as well as shop it alongside the RPC. When a cleaner thread finishes a cleaning pass, it increments the epoch as well as thus tags each cleaned segment alongside the novel epoch (after this point, no novel asking volition role the cleaned segment). The cleaner occasionally scans the epochs of active RPCs as well as frees the segment when all RPCs alongside epochs less than the segment's epoch have got completed. This approach creates additional overhead for segment freeing, but these operations are infrequent as well as run inwards a separate thread where they don't touching on read as well as write times.
Evaluation as well as in conclusion remarks
In the most stressful workload, a unmarried RAMCloud server tin dismiss back upwards 230,000-400,000 durable 100-byte writes per mo at 90% retentiveness utilization. The two-level approach to cleaning improves performance yesteryear 2-8x over a single-level approach at high retentiveness utilization, as well as reduces disk bandwidth overhead yesteryear 2-20x. Parallel cleaning effectively hides the toll of cleaning: an active cleaner adds exclusively close 3% to the latency of typical customer write requests. For detailed evaluation results, you lot tin dismiss check the paper.To recap, this function showed that logging likewise makes feel equally a technique for managing the information inwards DRAM: Log-structured retentiveness takes wages of /the restricted role of pointers/ inwards storage systems /to eliminate the global retentiveness scans/ that fundamentally bound existing garbage collectors. The resultant is an efficient as well as highly incremental shape of copying garbage collector that allows retentiveness to move used efficiently fifty-fifty at utilizations of 80-90%.
Questions for farther thinking along these lines:
"Each object inwards the log must move self-identifying; when the log is scanned during crash recovery, this information allows RAMCloud to position the most recent version of an object as well as reconstruct the hash table." Then, why non backup (copy reconstruct) the hash-table equally well?
"Log-structured retentiveness takes wages of the restricted role of pointers inwards storage systems to eliminate the global retentiveness scans that fundamentally bound existing garbage collectors." Can you lot hollo upwards of similar "restricted role of pointers" approaches to enable log crunch as well as parallel cleaning?
LSM worked for storage systems. What other applications/use-cases would this move appropriate? What applications/use-cases would this move inappropriate?
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