1. 08 Jun, 2018 1 commit
  2. 16 May, 2018 4 commits
  3. 15 May, 2018 1 commit
  4. 11 May, 2018 3 commits
  5. 07 May, 2018 4 commits
  6. 18 Apr, 2018 3 commits
  7. 16 Apr, 2018 3 commits
  8. 13 Apr, 2018 2 commits
  9. 12 Apr, 2018 2 commits
  10. 10 Apr, 2018 1 commit
  11. 29 Mar, 2018 2 commits
    • master: automatically discard feeding cells that get out-of-date · 3efbbfe3
      This is a follow-up of commit 2ca7c335,
      which changed 'tweak' not to discard readable cells too quickly.
      The scenario of a storage being lost whereas it has feeding cells was forgotten.
      These must be discarded immediately, otherwise we end up with more up-to-date
      cells than wanted. Without the change in outdate(), testSafeTweak would end
      with: UU.|U.U|UUU
      Once replication is optimized not to always restart checking cells from the
      - Remembering that an out-of-date cell was feeding could be a safer
        option, but it may not be worth the extra complexity.
      - Another possibility may be to replace the FEEDING state by an automatic
        partial tweak that only discards up-to-date cells too many whenever a cell
        becomes up-to-date.
      Julien Muchembled committed
  12. 20 Mar, 2018 2 commits
  13. 14 Mar, 2018 1 commit
    • storage: fix replication of creation undone · c3343279
      For records that undo object creation, None values are used at the backend
      level whereas the protocol is not designed to serialize None for any field.
      Therefore, a dance done in many places around packet serialization, using the
      specific 0/ZERO_HASH/'' triplet to represent a deleted oid. For replication,
      it was missing at the sender side, leading to the following crash:
        Traceback (most recent call last):
          File "neo/storage/app.py", line 147, in run
          File "neo/storage/app.py", line 178, in _run
          File "neo/storage/app.py", line 257, in doOperation
            next(task_queue[-1]) or task_queue.rotate()
          File "neo/storage/handlers/storage.py", line 271, in push
            conn.send(Packets.AddObject(oid, *object), msg_id)
          File "neo/lib/protocol.py", line 234, in __init__
            self._fmt.encode(buf.write, args)
          File "neo/lib/protocol.py", line 345, in encode
            return self._trace(self._encode, writer, items)
          File "neo/lib/protocol.py", line 334, in _trace
            return method(*args)
          File "neo/lib/protocol.py", line 367, in _encode
            item.encode(writer, value)
          File "neo/lib/protocol.py", line 345, in encode
            return self._trace(self._encode, writer, items)
          File "neo/lib/protocol.py", line 342, in _trace
            raise ParseError(self, trace)
        ParseError: at add_object/checksum:
          File "neo/lib/protocol.py", line 553, in _encode
            assert len(checksum) == 20, (len(checksum), checksum)
        TypeError: object of type 'NoneType' has no len()
      Julien Muchembled committed
  14. 13 Mar, 2018 1 commit
  15. 02 Mar, 2018 3 commits
  16. 17 Jan, 2018 1 commit
  17. 11 Jan, 2018 1 commit
  18. 08 Jan, 2018 1 commit
    • storage: optimize storage layout of raw data for replication · f4dd4bab
      # Previous status
      The issue was that we had extreme storage fragmentation from the point of view
      of the replication algorithm, which processes one partition at a time.
      By using an autoincrement for the 'data' table, rows were ordered by the time
      at which they were added:
      - parts may be the result of replication -> ordered by partition, tid, oid
      - other rows are globally sorted by tid
      Which means that when scanning a given partition, many rows were skipped all
      the time:
      - if readahead is bigger enough, the efficiency is 1/N for a node with N
        partitions assigned
      - else, it is worse because it seeks all the time
      For huge databases, the replication was horribly slow, in particular from HDD.
      # Chosen solution
      This commit changes how ids are generated to somehow split 'data'
      per partition. The backend tracks 1 last id per assigned partition, where the
      16 higher bits contains the partition. Keep in mind that the value of id has no
      meaning and it's only chosen for performance reasons. IOW, a row can be
      referred by an oid of a partition different than the 16 higher bits of id:
      - there's no migration needed and the 16 higher bits of all existing rows are 0
      - in case of deduplication, a row can still be shared by different partitions
      Due to https://jira.mariadb.org/browse/MDEV-12836, we leave the autoincrement
      on existing databases.
      ## Downsides
      On insertion, increasing the number of partitions now slows down significantly:
      for 2 nodes using TokuDB, 4% for 180 partitions, 40% for 2000. For 12
      partitions, the difference remains negligible. The solution for this issue will
      be to enable to increase the number of partitions efficiently, so that nodes
      can keep a small number of them, even for DB that are expected to grow so much
      that many nodes are added over time: such feature was already considered so
      that users don't have to worry anymore about this obscure setting at database
      Read performance is only slowed down for applications that read a lot of data
      that were written contiguously, but split in small blocks. A solution is to
      extend ZODB so that the application tells it to chose new oids that will end up
      in the same partition. Like for insertion, there should not be too many
      With RocksDB (MariaDB 10.2.10), it takes a significant amount of time to
      collect all last ids at startup when there are many partitions.
      ## Other advantages
      - The storage layout of data is now always the same and does not depend on
        whether rows came from replication or commits.
      - Efficient deletion of partition to free space in-place will be possible.
      # Considered alternative
      The only serious alternative was to replicate as many partitions as possible at
      the same time, ideally all assigned partitions, but it's not always possible.
      For best performance, it would often require to synchronize new nodes, or even
      all of them, so that thesource nodes don't have to scan 'data' several times.
      If existing nodes are kept, all data that aren't copied to the newly added
      nodes have to be skipped. If the number of nodes is multiplied by N, the
      efficiency is 1-1/N at best (synchronized nodes), else it's even worse
      because partitions are somehow shuffled.
      Checking/replacing a single node would remain slow when there are several
      source nodes.
      At last, such an algorithm would be much more complex and we would not have the
      other advantages listed above.
      Julien Muchembled committed
  19. 05 Jan, 2018 4 commits