WO2013188169A1 - Erasure coding and replication in storage clusters - Google Patents

Erasure coding and replication in storage clusters Download PDF

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Publication number
WO2013188169A1
WO2013188169A1 PCT/US2013/044045 US2013044045W WO2013188169A1 WO 2013188169 A1 WO2013188169 A1 WO 2013188169A1 US 2013044045 W US2013044045 W US 2013044045W WO 2013188169 A1 WO2013188169 A1 WO 2013188169A1
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WIPO (PCT)
Prior art keywords
cluster
digital object
storage cluster
storage
segment
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PCT/US2013/044045
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English (en)
French (fr)
Inventor
Don Baker
Paul R.M. Carpentier
Andrew KLAGER
Aaron PIERCE
Jonathan RING
Russell Turpin
David Yoakley
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Caringo Inc
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Caringo Inc
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Priority to CN201380043101.0A priority Critical patent/CN104541251B/zh
Priority to AU2013274626A priority patent/AU2013274626A1/en
Priority to EP13805176.8A priority patent/EP2862085A4/en
Priority to JP2015517293A priority patent/JP6280108B2/ja
Priority to CA2876638A priority patent/CA2876638A1/en
Publication of WO2013188169A1 publication Critical patent/WO2013188169A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Definitions

  • a manifest computer file is created that includes an indication of the erasure coding and a unique identifier within the storage cluster for each of the segments.
  • the storage cluster then stores the manifest computer file on one or more nodes of the cluster and returns a unique identifier of the manifest computer file to the client application. Manifests are distinguishable from other digital objects.
  • the invention behaves as described in the previous paragraph, to find the object, then as in the paragraph prior, to write it.
  • the new version will retain the previous version's unique identifier, but have a later creation timestamp, which distinguishes the two versions during the update process.
  • the older version is deleted once the newer version is successfully written in the cluster.
  • the health processing module may also delete older versions of objects for which newer versions are present, as a way of maintaining cluster data from error states.
  • metadata stored with an object or within cluster settings dictates when an object should be converted to a different format.
  • the cluster converts the object from a storage using replication to an erasure coding format, from one erasure coding format to another, or from an erasure coding format to a replication storage format.
  • the original object in its old format may be deleted if desired.
  • the unique identifier used with the original object is retained for use with the object in its new storage format, thus ensuring that the client application that originally stored the object may retrieve it at any future date using the original unique identifier with which it was provided.
  • FIG. 6 is a flow diagram describing how a storage cluster may recover from a volume failure.
  • erasure coding is a technique for providing redundancy of a data object without the overhead of replication.
  • erasure coding breaks the object into K data segments and from those data segments generates P parity segments, for a total of M segments in an erasure set, commonly indicated as a K:M erasure code.
  • K:M erasure code For example, a data object broken into 5 segments which are then used to generate 2 parity segments is referred to as using a 5:7 erasure code.
  • a key property of erasure codes is that the original object can be reconstructed from any K segments, whether a segment of an erasure-coded object is an original data segment or one of the parity segments.
  • the invention addresses exposure to data loss within a storage cluster by minimizing the time after a hardware failure before all missing segments are regenerated.
  • Each segment of an erasure-coded object includes a hint as to the volume identifier within the cluster that holds the next segment of the object. The hint is likely to be the correct volume identifier but may not be guaranteed.
  • a hardware failure such as a disk failure
  • each volume within the cluster will scan its journal on disk in order to find a segment having as its hint a volume identifier for the failed volume.
  • any missing segments can be identified and can be regenerated as quickly as possible, before waiting for any normal integrity checking of the cluster to occur.
  • Figure 1 illustrates an environment 100 for operation of the present invention. Included is a storage cluster 120, a client application 130, an administrative console 140, any number of computer nodes 10-50, and a central router 170.
  • a computer node is typically a physical file server that preferably includes at least one CPU and any number of disk drives 160, solid-state drives or hybrid drives that have both types.
  • the storage cluster 120 may be further logically or physically divided into sub clusters. For example, nodes 40 and 50 may be considered one subcluster, while nodes 10, 20 and 30 may be considered a second subcluster. Division of a cluster into sub-clusters may be advantageous where one subcluster is located in a different geographic location from another subcluster.
  • a storage cluster may be implemented using content storage software available from Caringo, Inc. of Austin, Texas (as modified as described herein), and any suitable computer hardware.
  • a storage cluster implements fixed-content content-addressable storage and each digital object is uniquely addressed within the cluster by a random number (a universally unique identifier, or UUID) that has been generated for that digital object using a random number generator. The contents of each digital object may be verified using a hash function.
  • a client software application receives the UUID when storing a digital object in the cluster and retrieves that digital object by supplying the UUID to the cluster.
  • Figure 2 illustrates an example erasure set 200 for an object using 5:7 erasure coding.
  • the data of the original object is separated into five data segments (kl-k5) 210-218, from which are generated two parity segments (pi and p2) 220 and 222.
  • the data is written, and the parity is generated, in stripes (stl-st9) 231-239.
  • the first stripe 231 consists of original data 251-255 from which are generated parity data 256 and 257. Any remaining data is formed in a final remainder stripe (rem) 240 and hash metadata may be stored at the end of each segment in a section 270.
  • an object (or stream) to be stored within the cluster may be broken into several erasure sets of a given size, the size being chosen for performance sake.
  • a very large object may be broken into several erasure sets, for example.
  • Within an erasure set the K data segments and the P parity segments are written using stripes with a fixed-size block of data written successively into each of the K data segments and then generating and writing the parity blocks to each of the P parity segments, each stripe (across the K + P segments) serving as an erasure coding unit.
  • the last stripe in a segment (remainder stripe 240, e.g.) may have a smaller block size that can be readily computed. Stripes are typically written until the incoming data is exhausted or until the given size for the erasure set has been filled, with subsequent data starting a new erasure set.
  • FIGs 4A and 4B are a flow diagram describing how a client application writes a file (a digital object) to the storage cluster.
  • any suitable client application 130 desires to store a digital object (such as any computer file, digital image, digital movie, health record, etc.) in the storage cluster 120.
  • the client application discovers or obtains an IP address of one of the nodes 10-50 within the cluster and chooses that node as the primary access node (PAN) with which to begin the storage process.
  • PAN primary access node
  • the client application sends a request to the PAN to store the digital object.
  • the SAN If the SAN has the lowest bid it responds by sending a "continue" message back to the client application. In response, the client sends the digital object to the SAN and the SAN stores the digital object, calculates a unique identifier and returns this identifier to the client application. On the other hand, if the SAN loses the bid, then the SAN redirects the client application to the node with the lowest bid that will then handle the request. The client application then sends the same write request to this node and the node responds by sending a "continue" message back to the client application. In response, the client sends the digital object to the node and the node stores the digital object, calculates a unique identifier and returns this identifier to the client application.
  • any these first K data segments are missing (step 520)
  • a request will be broadcast for any needed parity segments. For example, if two of the original data segments are missing, then a request must be broadcast for two of the parity segments using the unique identifiers from the manifest. If the needed number of parity segments are found, then in step 524 the missing data segment (or segments) is regenerated using the appropriate erasure coding algorithm and the found parity segments. In one embodiment, the hash value of the missing segment may be computed and compared to the original. Or, it is also possible to verify the data for each stripe by using a generated block as input with blocks from segments 1 to (K -1) to generate block K and compare that block against the original. If, though, K segments cannot be found, then an error message is returned to the client application.
  • FIG. 6 is a flow diagram describing how a storage cluster may recover from a volume failure.
  • a storage cluster includes any number of computer nodes, each node having any number of hard disks or solid-state disks, referred to as volumes.
  • a storage cluster typically achieves data redundancy by storing different replicas of an object on different nodes (when replication is used), and by storing the various data and parity segments of an object on different nodes (when erasure coding is used). Consequently, if a disk of a node fails, many of the replicas and segments of any number of objects will be lost, thus degrading the purported data redundancy of the entire storage cluster. Further, the quality of a storage cluster is judged not only by how many volumes it can afford to lose, but how fast the cluster can recover the missing data when a volume fails. Accordingly,
  • each node within the cluster is directed to scan all of its functioning volumes in order to identify streams that include a volume hint for the missing volume.
  • the node that has identified its missing volume sends a broadcast message (including the volume identifier) to all other nodes requesting a search for streams that have a hint for the missing volume.
  • the nodes will perform the search in parallel for efficiency.
  • the journal that each volume has recorded on disk is scanned and each stream representation is analyzed to determine the volume hint that it contains.
  • each stream representation in the journal that represents a segment of an erasure-coded object includes the volume identifier for the next segment
  • any such identified stream that include the volume identifier for the missing volume will also indicate a segment that was on the missing volume. For example, if the stream representation of segment 222 of Figure 2 includes a volume hint that is the volume identifier for the missing volume, this means that segment 210 was on that volume and needs to be regenerated.
  • representation of a replicated stream in the journal will include volume hints indicating the volume identifiers for all of the replicas of that stream.
  • the journal typically includes type information indicating whether the stream represents a replicated object or an erasure-coded object.
  • these volume hints may be stored in the system metadata 280 of a segment (or the metadata of a replicated stream).
  • the system metadata for segment 216 includes a volume hint indicating the volume identifier where segment 218 is stored. It may be possible for each node to scan its volumes looking for the system metadata of each stream on disk, although this technique will be slower. The volume hint may then be read from this system metadata. Again, a volume hint in a particular segment indicating the failed volume indicates that the next segment is missing.
  • the node identifies that a segment is missing, it can determine the unique identifier for that missing segment by looking at the metadata 280 of the previous segment and retrieving the unique identifiers for all sibling segments. In order to find any segments necessary for regenerating a missing segment, these unique identifiers of the sibling segments may be used.
  • the user metadata may dictate that within a specific time frame, or at a particular future time, that the object should be converted to different format.
  • storage cluster settings and rules may also dictate that objects shall be converted at a particular time or times, that objects of a certain size shall be converted periodically or at a particular time, or that a certain percentage of objects shall be converted.
  • the cluster may even accept manual inputs from an administrator that change cluster settings or that dictate how and when conversion should happen for an object or objects within the cluster.
  • a special conversion module may be used for performing conversion, or such functionality may be incorporated into the health processing module of the cluster.
  • Step 752 results when the trigger condition indicates that the object (currently stored using an old erasure coding) should be converted to a new erasure coding.
  • the unique identifier for the object is obtained from the object metadata and the cluster determines a node on which the manifest for the erasure-coded object exists.
  • this node reads the object from the cluster into memory. This step may be performed as discussed above with reference to Figure 5, specifically, steps 516-532.
  • step 760 the node writes the object to the cluster using the new erasure coding format determined from user metadata, system metadata, cluster settings, or administrator input. This step may be performed as discussed above with reference to Figures 4A and 4B, specifically, steps 416-448.
  • the object may be copied from the source cluster in many different ways.
  • the source cluster may read the object and then "push” it to the target cluster, or, the target cluster may "pull" the object from the source cluster.
  • a target node is first selected in the target cluster to perform the write of the object within the target cluster.
  • the target node may be selected randomly, by using a bid process, or other technique.
  • the target node is provided with the unique identifier for the object to be copied and contact information for the source cluster.
  • the target node may be provided with a communication address for the source cluster overall, with an address of a central or coordinating node within the cluster, or preferably, an IP address of any node within the source cluster.
  • any relevant conversion information is identified within the target cluster. For example, any default settings or rules that specify how the copied object should be stored within the target cluster (i.e., using replication or erasure coding) are identified. If no default settings are relevant then the conversion information may be taken from the user metadata contained within the object to be copied. Alternatively, the instruction to copy the object may include the conversion information.
  • step 816 the object is copied from the source cluster to the target cluster.
  • the target node initiates copying of the object by contacting any node of the source cluster using the IP address provided and provides the unique identifier for the object.
  • the object may then be communicated from the source cluster to memory of the target node. This step may be performed, for example, as explained above with reference to Figure 5, where the target node is acting as the client application.
  • the target node receives the object (or as the target node is received and the object) it will write the note into the target cluster using the appropriate conversion information that has been determined above. In other words, the object will be written as a continuous stream (replication) or will be written using erasure coding.
  • this write step may be performed as described above with reference to Figure 4A and 4B where the target node acts as the secondary access node.
  • the target node may solicit bids from the other nodes within the target cluster, or may write the object to one of its own disks.
  • the segments will be written to various nodes within the target cluster.
  • the copied object in the target cluster retains the same unique identifier it had in the source cluster. Once the object has been copied to the target cluster, it may be retained in the source cluster or deleted at a future time.

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CA2876638A1 (en) 2013-12-19
US20150019937A1 (en) 2015-01-15
JP2015519674A (ja) 2015-07-09
US10437672B2 (en) 2019-10-08
US20130339818A1 (en) 2013-12-19
AU2013274626A1 (en) 2015-01-22
CN104541251A (zh) 2015-04-22
US9148174B2 (en) 2015-09-29
US20150363269A1 (en) 2015-12-17
US20170351575A1 (en) 2017-12-07
EP2862085A1 (en) 2015-04-22
CN104541251B (zh) 2017-09-05
US8799746B2 (en) 2014-08-05
US9916198B2 (en) 2018-03-13

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