valid := 0
									for shardIdx, shard := range splitFilled[:k] {
										shardConfig[shardIdx] = shard[offset]
										valid += int(shard[offset])
										if shard[offset] == 0 {
											shards[shardIdx] = shards[shardIdx][:0]
										} else {
											shards[shardIdx] = append(shards[shardIdx][:0], splitData[shardIdx][offset])
										}
									}

	StandardEnv = "MINIO_STORAGE_CLASS_STANDARD"
	// Optimize storage class environment variable
	OptimizeEnv = "MINIO_STORAGE_CLASS_OPTIMIZE"
	// Inline block indicates the size of the shard
	// that is considered for inlining, remember this
	// shard value is the value per drive shard it
	// will vary based on the parity that is configured
	// for the STANDARD storage_class.
	// inlining means data and metadata are written

			if test.reconstructParity {
				for j := range decoded {
					if decoded[j] == nil {
						t.Errorf("Test %d: failed to reconstruct shard %d", i, j)
					}
				}
			} else {
				for j := range decoded[:test.dataBlocks] {
					if decoded[j] == nil {
						t.Errorf("Test %d: failed to reconstruct data shard %d", i, j)
					}
				}
			}

			decodedData := new(bytes.Buffer)

    /**
     * Gets the number of shards for the queue index.
     *
     * @return the number of queue shards
     */
    @Override
    public int getQueueShards() {
        return ComponentUtil.getFessConfig().getIndexDocumentCrawlerQueueNumberOfShardsAsInteger();
    }

    /**
     * Gets the number of shards for the data index.
     *
     * @return the number of data shards
     */
    @Override

code is a mathematical algorithm to reconstruct missing or corrupted data. MinIO uses Reed-Solomon code to shard objects into variable data and parity blocks. For example, in a 12 drive setup, an object can be sharded to a variable number of data and parity blocks across all the drives - ranging from six data and six parity blocks to ten data and two parity blocks.

By default, MinIO shards the objects across N/2 data and N/2 parity drives. Though, you can use [storage classes](https://github...

				fmt.Fprintf(os.Stderr, "%v: error on self-test [d:%d,p:%d]: want %#v, got %#v\n", algo, conf[0], conf[1], a, b)
				ok = false
				continue
			}
			// Delete first shard and reconstruct...
			first := encoded[0]
			encoded[0] = nil
			failOnErr(e.DecodeDataBlocks(encoded))
			if a, b := first, encoded[0]; !bytes.Equal(a, b) {

    public String getWriteableName() {
        return queryBuilder.getWriteableName();
    }

    /**
     * Creates a Lucene Query from this query builder.
     *
     * @param context the query shard context
     * @return the Lucene Query
     * @throws IOException if an I/O error occurs
     */
    @Override
    public Query toQuery(final QueryShardContext context) throws IOException {

- Erasure coding used by MinIO is [Reed-Solomon](https://github.com/klauspost/reedsolomon) erasure coding scheme, which has a total shard maximum of 256 i.e 128 data and 128 parity. MinIO design goes beyond this limitation by doing some practical architecture choices.

	shardSize := int64(1024 * 1024)
	shard := make([]byte, shardSize)
	w := newStreamingBitrotWriter(storage, "", volName, fileName, size, algo, shardSize)
	reader := bytes.NewReader(data)
	for {
		// Using io.Copy instead of this loop will not work for us as io.Copy
		// will use bytes.Reader.WriteTo() which will not do shardSize'ed writes
		// causing error.
		n, err := reader.Read(shard)
		w.Write(shard[:n])

	// To avoid these problems we must split the work at scale. With 1000 node
	// setup becoming a reality we must try to shard the work properly such as
	// pick 10 nodes that precisely can send those 100 requests the first node
	// in the 10 node shard would coordinate between other 9 shards to get the
	// rest of the `99*9` requests.
	//
	// This essentially splits the workload properly and also allows for network