HBase and Schema Design

- - Schema Creation - - HBase schemas can be created or updated with - or by using HBaseAdmin in the Java API. - - Tables must be disabled when making ColumnFamily modifications, for example: - + A good general introduction on the strength and weaknesses modelling on the various + non-rdbms datastores is Ian Varley's Master thesis, No Relation: + The Mixed Blessings of Non-Relational Databases. Recommended. Also, read for how HBase stores data internally, and the section on . +

+ Schema Creation + HBase schemas can be created or updated with or by using HBaseAdmin + in the Java API. + Tables must be disabled when making ColumnFamily modifications, for example: + Configuration config = HBaseConfiguration.create(); HBaseAdmin admin = new HBaseAdmin(conf); String table = "myTable"; @@ -55,110 +57,134 @@ HColumnDescriptor cf2 = ...; admin.modifyColumn(table, cf2); // modifying existing ColumnFamily admin.enableTable(table); - - See for more information about configuring client connections. - Note: online schema changes are supported in the 0.92.x codebase, but the 0.90.x codebase requires the table - to be disabled. - -

Schema Updates - When changes are made to either Tables or ColumnFamilies (e.g., region size, block size), these changes - take effect the next time there is a major compaction and the StoreFiles get re-written. - - See for more information on StoreFiles. - + + See for more information about configuring client + connections. + Note: online schema changes are supported in the 0.92.x codebase, but the 0.90.x codebase + requires the table to be disabled. +

+ Schema Updates + When changes are made to either Tables or ColumnFamilies (e.g., region size, block + size), these changes take effect the next time there is a major compaction and the + StoreFiles get re-written. + See for more information on StoreFiles.

- - On the number of column families - - - HBase currently does not do well with anything above two or three column families so keep the number - of column families in your schema low. Currently, flushing and compactions are done on a per Region basis so - if one column family is carrying the bulk of the data bringing on flushes, the adjacent families - will also be flushed though the amount of data they carry is small. When many column families the - flushing and compaction interaction can make for a bunch of needless i/o loading (To be addressed by - changing flushing and compaction to work on a per column family basis). For more information - on compactions, see . - - Try to make do with one column family if you can in your schemas. Only introduce a - second and third column family in the case where data access is usually column scoped; - i.e. you query one column family or the other but usually not both at the one time. - -

Cardinality of ColumnFamilies - Where multiple ColumnFamilies exist in a single table, be aware of the cardinality (i.e., number of rows). - If ColumnFamilyA has 1 million rows and ColumnFamilyB has 1 billion rows, ColumnFamilyA's data will likely be spread - across many, many regions (and RegionServers). This makes mass scans for ColumnFamilyA less efficient. - +

+ On the number of column families + HBase currently does not do well with anything above two or three column families so keep + the number of column families in your schema low. Currently, flushing and compactions are done + on a per Region basis so if one column family is carrying the bulk of the data bringing on + flushes, the adjacent families will also be flushed though the amount of data they carry is + small. When many column families the flushing and compaction interaction can make for a bunch + of needless i/o loading (To be addressed by changing flushing and compaction to work on a per + column family basis). For more information on compactions, see . + Try to make do with one column family if you can in your schemas. Only introduce a second + and third column family in the case where data access is usually column scoped; i.e. you query + one column family or the other but usually not both at the one time. +

+ Cardinality of ColumnFamilies + Where multiple ColumnFamilies exist in a single table, be aware of the cardinality + (i.e., number of rows). If ColumnFamilyA has 1 million rows and ColumnFamilyB has 1 billion + rows, ColumnFamilyA's data will likely be spread across many, many regions (and + RegionServers). This makes mass scans for ColumnFamilyA less efficient.

Rowkey Design -

- - Monotonically Increasing Row Keys/Timeseries Data - - - In the HBase chapter of Tom White's book Hadoop: The Definitive Guide (O'Reilly) there is a an optimization note on watching out for a phenomenon where an import process walks in lock-step with all clients in concert pounding one of the table's regions (and thus, a single node), then moving onto the next region, etc. With monotonically increasing row-keys (i.e., using a timestamp), this will happen. See this comic by IKai Lan on why monotonically increasing row keys are problematic in BigTable-like datastores: - monotonically increasing values are bad. The pile-up on a single region brought on - by monotonically increasing keys can be mitigated by randomizing the input records to not be in sorted order, but in general it's best to avoid using a timestamp or a sequence (e.g. 1, 2, 3) as the row-key. - - If you do need to upload time series data into HBase, you should - study OpenTSDB as a - successful example. It has a page describing the schema it uses in - HBase. The key format in OpenTSDB is effectively [metric_type][event_timestamp], which would appear at first glance to contradict the previous advice about not using a timestamp as the key. However, the difference is that the timestamp is not in the lead position of the key, and the design assumption is that there are dozens or hundreds (or more) of different metric types. Thus, even with a continual stream of input data with a mix of metric types, the Puts are distributed across various points of regions in the table. - - See for some rowkey design examples. - -

+ Rowkey Design +

+ Monotonically Increasing Row Keys/Timeseries Data + In the HBase chapter of Tom White's book Hadoop: The Definitive Guide + (O'Reilly) there is a an optimization note on watching out for a phenomenon where an import + process walks in lock-step with all clients in concert pounding one of the table's regions + (and thus, a single node), then moving onto the next region, etc. With monotonically + increasing row-keys (i.e., using a timestamp), this will happen. See this comic by IKai Lan + on why monotonically increasing row keys are problematic in BigTable-like datastores: monotonically + increasing values are bad. The pile-up on a single region brought on by + monotonically increasing keys can be mitigated by randomizing the input records to not be in + sorted order, but in general it's best to avoid using a timestamp or a sequence (e.g. 1, 2, + 3) as the row-key. + If you do need to upload time series data into HBase, you should study OpenTSDB as a successful example. It has a page + describing the schema it uses in HBase. The key + format in OpenTSDB is effectively [metric_type][event_timestamp], which would appear at + first glance to contradict the previous advice about not using a timestamp as the key. + However, the difference is that the timestamp is not in the lead + position of the key, and the design assumption is that there are dozens or hundreds (or + more) of different metric types. Thus, even with a continual stream of input data with a mix + of metric types, the Puts are distributed across various points of regions in the table. + See for some rowkey design examples. +

Try to minimize row and column sizes Or why are my StoreFile indices large? - In HBase, values are always freighted with their coordinates; as a - cell value passes through the system, it'll be accompanied by its - row, column name, and timestamp - always. If your rows and column names - are large, especially compared to the size of the cell value, then - you may run up against some interesting scenarios. One such is - the case described by Marc Limotte at the tail of - HBASE-3551 - (recommended!). - Therein, the indices that are kept on HBase storefiles () - to facilitate random access may end up occupyng large chunks of the HBase - allotted RAM because the cell value coordinates are large. - Mark in the above cited comment suggests upping the block size so - entries in the store file index happen at a larger interval or - modify the table schema so it makes for smaller rows and column - names. - Compression will also make for larger indices. See - the thread a question storefileIndexSize - up on the user mailing list. - - Most of the time small inefficiencies don't matter all that much. Unfortunately, - this is a case where they do. Whatever patterns are selected for ColumnFamilies, attributes, and rowkeys they could be repeated - several billion times in your data. - See for more information on HBase stores data internally to see why this is important. -

Column Families - Try to keep the ColumnFamily names as small as possible, preferably one character (e.g. "d" for data/default). - - See for more information on HBase stores data internally to see why this is important. -

Attributes - Although verbose attribute names (e.g., "myVeryImportantAttribute") are easier to read, prefer shorter attribute names (e.g., "via") - to store in HBase. - - See for more information on HBase stores data internally to see why this is important. -

Rowkey Length - Keep them as short as is reasonable such that they can still be useful for required data access (e.g., Get vs. Scan). - A short key that is useless for data access is not better than a longer key with better get/scan properties. Expect tradeoffs - when designing rowkeys. - -

Byte Patterns - A long is 8 bytes. You can store an unsigned number up to 18,446,744,073,709,551,615 in those eight bytes. - If you stored this number as a String -- presuming a byte per character -- you need nearly 3x the bytes. - - Not convinced? Below is some sample code that you can run on your own. - + In HBase, values are always freighted with their coordinates; as a cell value passes + through the system, it'll be accompanied by its row, column name, and timestamp - always. If + your rows and column names are large, especially compared to the size of the cell value, + then you may run up against some interesting scenarios. One such is the case described by + Marc Limotte at the tail of HBASE-3551 + (recommended!). Therein, the indices that are kept on HBase storefiles () to facilitate random access may end up occupyng large chunks of the + HBase allotted RAM because the cell value coordinates are large. Mark in the above cited + comment suggests upping the block size so entries in the store file index happen at a larger + interval or modify the table schema so it makes for smaller rows and column names. + Compression will also make for larger indices. See the thread a + question storefileIndexSize up on the user mailing list. + Most of the time small inefficiencies don't matter all that much. Unfortunately, this is + a case where they do. Whatever patterns are selected for ColumnFamilies, attributes, and + rowkeys they could be repeated several billion times in your data. + See for more information on HBase stores data internally to see why this + is important. +

+ Column Families + Try to keep the ColumnFamily names as small as possible, preferably one character + (e.g. "d" for data/default). + See for more information on HBase stores data internally to see why + this is important. +

+ Attributes + Although verbose attribute names (e.g., "myVeryImportantAttribute") are easier to + read, prefer shorter attribute names (e.g., "via") to store in HBase. + See for more information on HBase stores data internally to see why + this is important. +

+ Rowkey Length + Keep them as short as is reasonable such that they can still be useful for required + data access (e.g., Get vs. Scan). A short key that is useless for data access is not + better than a longer key with better get/scan properties. Expect tradeoffs when designing + rowkeys. +

+ Byte Patterns + A long is 8 bytes. You can store an unsigned number up to 18,446,744,073,709,551,615 + in those eight bytes. If you stored this number as a String -- presuming a byte per + character -- you need nearly 3x the bytes. + Not convinced? Below is some sample code that you can run on your own. + // long // long l = 1234567890L; @@ -178,11 +204,11 @@ System.out.println("md5 digest bytes length: " + digest.length); // returns 1 String sDigest = new String(digest); byte[] sbDigest = Bytes.toBytes(sDigest); System.out.println("md5 digest as string length: " + sbDigest.length); // returns 26 - - - Unfortunately, using a binary representation of a type will make your data harder to read outside of your code. For example, - this is what you will see in the shell when you increment a value: - + + Unfortunately, using a binary representation of a type will make your data harder to + read outside of your code. For example, this is what you will see in the shell when you + increment a value: + hbase(main):001:0> incr 't', 'r', 'f:q', 1 COUNTER VALUE = 1 @@ -190,53 +216,67 @@ hbase(main):002:0> get 't', 'r' COLUMN CELL f:q timestamp=1369163040570, value=\x00\x00\x00\x00\x00\x00\x00\x01 1 row(s) in 0.0310 seconds - - The shell makes a best effort to print a string, and it this case it decided to just print the hex. The same will - happen to your row keys inside the region names. It can be okay if you know what's being stored, but it might also - be unreadable if arbitrary data can be put in the same cells. This is the main trade-off. - -

+ + The shell makes a best effort to print a string, and it this case it decided to just + print the hex. The same will happen to your row keys inside the region names. It can be + okay if you know what's being stored, but it might also be unreadable if arbitrary data + can be put in the same cells. This is the main trade-off. +

Reverse Timestamps - - Reverse Scan API - - HBASE-4811 implements an API to scan a table or a range within a table in reverse, reducing the need to optimize your schema for forward or reverse scanning. This feature is available in HBase 0.98 and later. See for more information. - - +

+ Reverse Timestamps + + Reverse Scan API + + HBASE-4811 + implements an API to scan a table or a range within a table in reverse, reducing the need + to optimize your schema for forward or reverse scanning. This feature is available in + HBase 0.98 and later. See + for more information. + - A common problem in database processing is quickly finding the most recent version of a value. A technique using reverse timestamps - as a part of the key can help greatly with a special case of this problem. Also found in the HBase chapter of Tom White's book Hadoop: The Definitive Guide (O'Reilly), - the technique involves appending (Long.MAX_VALUE - timestamp) to the end of any key, e.g., [key][reverse_timestamp]. - - The most recent value for [key] in a table can be found by performing a Scan for [key] and obtaining the first record. Since HBase keys - are in sorted order, this key sorts before any older row-keys for [key] and thus is first. - - This technique would be used instead of using where the intent is to hold onto all versions - "forever" (or a very long time) and at the same time quickly obtain access to any other version by using the same Scan technique. - + A common problem in database processing is quickly finding the most recent version of a + value. A technique using reverse timestamps as a part of the key can help greatly with a + special case of this problem. Also found in the HBase chapter of Tom White's book Hadoop: + The Definitive Guide (O'Reilly), the technique involves appending (

Long.MAX_VALUE -
+          timestamp

) to the end of any key, e.g., [key][reverse_timestamp]. + The most recent value for [key] in a table can be found by performing a Scan for [key] + and obtaining the first record. Since HBase keys are in sorted order, this key sorts before + any older row-keys for [key] and thus is first. + This technique would be used instead of using where the intent is to hold onto all versions "forever" (or a + very long time) and at the same time quickly obtain access to any other version by using the + same Scan technique.

- Rowkeys and ColumnFamilies - Rowkeys are scoped to ColumnFamilies. Thus, the same rowkey could exist in each ColumnFamily that exists in a table without collision. - +

+ Rowkeys and ColumnFamilies + Rowkeys are scoped to ColumnFamilies. Thus, the same rowkey could exist in each + ColumnFamily that exists in a table without collision.

Immutability of Rowkeys - Rowkeys cannot be changed. The only way they can be "changed" in a table is if the row is deleted and then re-inserted. - This is a fairly common question on the HBase dist-list so it pays to get the rowkeys right the first time (and/or before you've - inserted a lot of data). - +

+ Immutability of Rowkeys + Rowkeys cannot be changed. The only way they can be "changed" in a table is if the row + is deleted and then re-inserted. This is a fairly common question on the HBase dist-list so + it pays to get the rowkeys right the first time (and/or before you've inserted a lot of + data).

Relationship Between RowKeys and Region Splits - If you pre-split your table, it is critical to understand how your rowkey will be distributed across - the region boundaries. As an example of why this is important, consider the example of using displayable hex characters as the - lead position of the key (e.g., "0000000000000000" to "ffffffffffffffff"). Running those key ranges through Bytes.split - (which is the split strategy used when creating regions in HBaseAdmin.createTable(byte[] startKey, byte[] endKey, numRegions) - for 10 regions will generate the following splits... - - - +

+ Relationship Between RowKeys and Region Splits + If you pre-split your table, it is critical to understand how your + rowkey will be distributed across the region boundaries. As an example of why this is + important, consider the example of using displayable hex characters as the lead position of + the key (e.g., "0000000000000000" to "ffffffffffffffff"). Running those + key ranges through Bytes.split (which is the split strategy used when creating + regions in HBaseAdmin.createTable(byte[] startKey, byte[] endKey, numRegions) + for 10 regions will generate the following splits... + 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 // 0 54 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 // 6 61 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -68 // = @@ -246,27 +286,28 @@ COLUMN CELL 88 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -44 // X 95 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -102 // _ 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 // f - - ... (note: the lead byte is listed to the right as a comment.) Given that the first split is a '0' and the last split is an 'f', - everything is great, right? Not so fast. - - The problem is that all the data is going to pile up in the first 2 regions and the last region thus creating a "lumpy" (and - possibly "hot") region problem. To understand why, refer to an ASCII Table. - '0' is byte 48, and 'f' is byte 102, but there is a huge gap in byte values (bytes 58 to 96) that will never appear in this - keyspace because the only values are [0-9] and [a-f]. Thus, the middle regions regions will - never be used. To make pre-spliting work with this example keyspace, a custom definition of splits (i.e., and not relying on the - built-in split method) is required. - - Lesson #1: Pre-splitting tables is generally a best practice, but you need to pre-split them in such a way that all the - regions are accessible in the keyspace. While this example demonstrated the problem with a hex-key keyspace, the same problem can happen - with any keyspace. Know your data. - - Lesson #2: While generally not advisable, using hex-keys (and more generally, displayable data) can still work with pre-split - tables as long as all the created regions are accessible in the keyspace. - - To conclude this example, the following is an example of how appropriate splits can be pre-created for hex-keys:. - -public static boolean createTable(HBaseAdmin admin, HTableDescriptor table, byte[][] splits) + + ... (note: the lead byte is listed to the right as a comment.) Given that the first + split is a '0' and the last split is an 'f', everything is great, right? Not so fast. + The problem is that all the data is going to pile up in the first 2 regions and the last + region thus creating a "lumpy" (and possibly "hot") region problem. To understand why, refer + to an ASCII Table. '0' is byte 48, and 'f' is byte + 102, but there is a huge gap in byte values (bytes 58 to 96) that will never + appear in this keyspace because the only values are [0-9] and [a-f]. Thus, the + middle regions regions will never be used. To make pre-spliting work with this example + keyspace, a custom definition of splits (i.e., and not relying on the built-in split method) + is required. + Lesson #1: Pre-splitting tables is generally a best practice, but you need to pre-split + them in such a way that all the regions are accessible in the keyspace. While this example + demonstrated the problem with a hex-key keyspace, the same problem can happen with + any keyspace. Know your data. + Lesson #2: While generally not advisable, using hex-keys (and more generally, + displayable data) can still work with pre-split tables as long as all the created regions + are accessible in the keyspace. + To conclude this example, the following is an example of how appropriate splits can be + pre-created for hex-keys:. + +}]]>

- - Number of Versions - -

Maximum Number of Versions - The maximum number of row versions to store is configured per column - family via HColumnDescriptor. - The default for max versions is 1. - This is an important parameter because as described in - section HBase does not overwrite row values, but rather - stores different values per row by time (and qualifier). Excess versions are removed during major - compactions. The number of max versions may need to be increased or decreased depending on application needs. - - It is not recommended setting the number of max versions to an exceedingly high level (e.g., hundreds or more) unless those old values are - very dear to you because this will greatly increase StoreFile size. - -

- - Minimum Number of Versions - - Like maximum number of row versions, the minimum number of row versions to keep is configured per column - family via HColumnDescriptor. - The default for min versions is 0, which means the feature is disabled. - The minimum number of row versions parameter is used together with the time-to-live parameter and can be combined with the - number of row versions parameter to allow configurations such as - "keep the last T minutes worth of data, at most N versions, but keep at least M versions around" - (where M is the value for minimum number of row versions, M<N). - This parameter should only be set when time-to-live is enabled for a column family and must be less than the - number of row versions. - +

+ +

+ Number of Versions +

+ Maximum Number of Versions + The maximum number of row versions to store is configured per column family via HColumnDescriptor. + The default for max versions is 1. This is an important parameter because as described in section HBase does not overwrite row values, + but rather stores different values per row by time (and qualifier). Excess versions are + removed during major compactions. The number of max versions may need to be increased or + decreased depending on application needs. + It is not recommended setting the number of max versions to an exceedingly high level + (e.g., hundreds or more) unless those old values are very dear to you because this will + greatly increase StoreFile size. +

+ Minimum Number of Versions + Like maximum number of row versions, the minimum number of row versions to keep is + configured per column family via HColumnDescriptor. + The default for min versions is 0, which means the feature is disabled. The minimum number + of row versions parameter is used together with the time-to-live parameter and can be + combined with the number of row versions parameter to allow configurations such as "keep the + last T minutes worth of data, at most N versions, but keep at least M versions + around" (where M is the value for minimum number of row versions, M<N). This + parameter should only be set when time-to-live is enabled for a column family and must be + less than the number of row versions.

- - Supported Datatypes - - HBase supports a "bytes-in/bytes-out" interface via Put and - Result, so anything that can be - converted to an array of bytes can be stored as a value. Input could be strings, numbers, complex objects, or even images as long as they can rendered as bytes. - - There are practical limits to the size of values (e.g., storing 10-50MB objects in HBase would probably be too much to ask); - search the mailling list for conversations on this topic. All rows in HBase conform to the , and - that includes versioning. Take that into consideration when making your design, as well as block size for the ColumnFamily. - +

+ Supported Datatypes + HBase supports a "bytes-in/bytes-out" interface via Put + and Result, + so anything that can be converted to an array of bytes can be stored as a value. Input could + be strings, numbers, complex objects, or even images as long as they can rendered as bytes. + There are practical limits to the size of values (e.g., storing 10-50MB objects in HBase + would probably be too much to ask); search the mailling list for conversations on this topic. + All rows in HBase conform to the , and that includes versioning. Take that into consideration when + making your design, as well as block size for the ColumnFamily. -

Counters - - One supported datatype that deserves special mention are "counters" (i.e., the ability to do atomic increments of numbers). See - Increment in HTable. - - Synchronization on counters are done on the RegionServer, not in the client. - + One supported datatype that deserves special mention are "counters" (i.e., the ability + to do atomic increments of numbers). See Increment + in HTable. + Synchronization on counters are done on the RegionServer, not in the client.

Joins - If you have multiple tables, don't forget to factor in the potential for into the schema design. - +

+ Joins + If you have multiple tables, don't forget to factor in the potential for into the schema design.

- Time To Live (TTL) - ColumnFamilies can set a TTL length in seconds, and HBase will automatically delete rows once the expiration time is reached. - This applies to all versions of a row - even the current one. The TTL time encoded in the HBase for the row is specified in UTC. - - See HColumnDescriptor for more information. - +

+ Time To Live (TTL) + ColumnFamilies can set a TTL length in seconds, and HBase will automatically delete rows + once the expiration time is reached. This applies to all versions of a + row - even the current one. The TTL time encoded in the HBase for the row is specified in UTC. + See HColumnDescriptor + for more information.

- - Keeping Deleted Cells - - ColumnFamilies can optionally keep deleted cells. That means deleted cells can still be retrieved with - Get or - Scan operations, - as long these operations have a time range specified that ends before the timestamp of any delete that would affect the cells. - This allows for point in time queries even in the presence of deletes. - - - Deleted cells are still subject to TTL and there will never be more than "maximum number of versions" deleted cells. - A new "raw" scan options returns all deleted rows and the delete markers. - - See HColumnDescriptor for more information. - +

+ Keeping Deleted Cells + ColumnFamilies can optionally keep deleted cells. That means deleted cells can still be + retrieved with Get + or Scan + operations, as long these operations have a time range specified that ends before the + timestamp of any delete that would affect the cells. This allows for point in time queries + even in the presence of deletes. + Deleted cells are still subject to TTL and there will never be more than "maximum number + of versions" deleted cells. A new "raw" scan options returns all deleted rows and the delete + markers. + See HColumnDescriptor + for more information.

- - Secondary Indexes and Alternate Query Paths - - This section could also be titled "what if my table rowkey looks like this but I also want to query my table like that." - A common example on the dist-list is where a row-key is of the format "user-timestamp" but there are reporting requirements on activity across users for certain - time ranges. Thus, selecting by user is easy because it is in the lead position of the key, but time is not. - - There is no single answer on the best way to handle this because it depends on... - - Number of users - Data size and data arrival rate - Flexibility of reporting requirements (e.g., completely ad-hoc date selection vs. pre-configured ranges) - Desired execution speed of query (e.g., 90 seconds may be reasonable to some for an ad-hoc report, whereas it may be too long for others) - - ... and solutions are also influenced by the size of the cluster and how much processing power you have to throw at the solution. - Common techniques are in sub-sections below. This is a comprehensive, but not exhaustive, list of approaches. - - It should not be a surprise that secondary indexes require additional cluster space and +

+ Secondary Indexes and Alternate Query Paths + This section could also be titled "what if my table rowkey looks like + this but I also want to query my table like that." + A common example on the dist-list is where a row-key is of the format "user-timestamp" but + there are reporting requirements on activity across users for certain time ranges. Thus, + selecting by user is easy because it is in the lead position of the key, but time is not. + There is no single answer on the best way to handle this because it depends on... + + + Number of users + + + Data size and data arrival rate + + + Flexibility of reporting requirements (e.g., completely ad-hoc date selection vs. + pre-configured ranges) + + + Desired execution speed of query (e.g., 90 seconds may be reasonable to some for an + ad-hoc report, whereas it may be too long for others) + + + ... and solutions are also influenced by the size of the cluster and how much processing + power you have to throw at the solution. Common techniques are in sub-sections below. This is + a comprehensive, but not exhaustive, list of approaches. + It should not be a surprise that secondary indexes require additional cluster space and processing. This is precisely what happens in an RDBMS because the act of creating an alternate index requires both space and processing cycles to update. RDBMS products are more advanced in this regard to handle alternative index management out of the box. However, HBase scales better at larger data volumes, so this is a feature trade-off. - Pay attention to when implementing any of these approaches. - Additionally, see the David Butler response in this dist-list thread HBase, mail # user - Stargate+hbase - -

- - Filter Query - - Depending on the case, it may be appropriate to use . In this case, no secondary index is created. - However, don't try a full-scan on a large table like this from an application (i.e., single-threaded client). - + Pay attention to when implementing any of these approaches. + Additionally, see the David Butler response in this dist-list thread HBase, + mail # user - Stargate+hbase + +

+ Filter Query + Depending on the case, it may be appropriate to use . In this case, no secondary index is created. However, don't + try a full-scan on a large table like this from an application (i.e., single-threaded + client).

- - Periodic-Update Secondary Index - - A secondary index could be created in an other table which is periodically updated via a MapReduce job. The job could be executed intra-day, but depending on - load-strategy it could still potentially be out of sync with the main data table. - See for more information. +

+ Periodic-Update Secondary Index + A secondary index could be created in an other table which is periodically updated via a + MapReduce job. The job could be executed intra-day, but depending on load-strategy it could + still potentially be out of sync with the main data table. + See for more information.

- - Dual-Write Secondary Index - - Another strategy is to build the secondary index while publishing data to the cluster (e.g., write to data table, write to index table). - If this is approach is taken after a data table already exists, then bootstrapping will be needed for the secondary index with a MapReduce job (see ). +

+ Dual-Write Secondary Index + Another strategy is to build the secondary index while publishing data to the cluster + (e.g., write to data table, write to index table). If this is approach is taken after a data + table already exists, then bootstrapping will be needed for the secondary index with a + MapReduce job (see ).

- - Summary Tables - - Where time-ranges are very wide (e.g., year-long report) and where the data is voluminous, summary tables are a common approach. - These would be generated with MapReduce jobs into another table. - See for more information. +

+ Summary Tables + Where time-ranges are very wide (e.g., year-long report) and where the data is + voluminous, summary tables are a common approach. These would be generated with MapReduce + jobs into another table. + See for more information.

- - Coprocessor Secondary Index - - Coprocessors act like RDBMS triggers. These were added in 0.92. For more information, see +

+ Coprocessor Secondary Index + Coprocessors act like RDBMS triggers. These were added in 0.92. For more information, + see

Constraints - HBase currently supports 'constraints' in traditional (SQL) database parlance. The advised usage for Constraints is in enforcing business rules for attributes in the table (eg. make sure values are in the range 1-10). - Constraints could also be used to enforce referential integrity, but this is strongly discouraged as it will dramatically decrease the write throughput of the tables where integrity checking is enabled. - Extensive documentation on using Constraints can be found at: Constraint since version 0.94. - +

+ Constraints + HBase currently supports 'constraints' in traditional (SQL) database parlance. The advised + usage for Constraints is in enforcing business rules for attributes in the table (eg. make + sure values are in the range 1-10). Constraints could also be used to enforce referential + integrity, but this is strongly discouraged as it will dramatically decrease the write + throughput of the tables where integrity checking is enabled. Extensive documentation on using + Constraints can be found at: Constraint + since version 0.94.

Schema Design Case Studies - The following will describe some typical data ingestion use-cases with HBase, and how the rowkey design and construction - can be approached. Note: this is just an illustration of potential approaches, not an exhaustive list. - Know your data, and know your processing requirements. - - It is highly recommended that you read the rest of the first, before reading - these case studies. - - Thee following case studies are described: - - Log Data / Timeseries Data - Log Data / Timeseries on Steroids - Customer/Order - Tall/Wide/Middle Schema Design - List Data - - -

+ Schema Design Case Studies + The following will describe some typical data ingestion use-cases with HBase, and how the + rowkey design and construction can be approached. Note: this is just an illustration of + potential approaches, not an exhaustive list. Know your data, and know your processing + requirements. + It is highly recommended that you read the rest of the first, before reading these case studies. + The following case studies are described: + + + Log Data / Timeseries Data + + + Log Data / Timeseries on Steroids + + + Customer/Order + + + Tall/Wide/Middle Schema Design + + + List Data + + +

Case Study - Log Data and Timeseries Data - Assume that the following data elements are being collected. - - Hostname - Timestamp - Log event - Value/message - - We can store them in an HBase table called LOG_DATA, but what will the rowkey be? - From these attributes the rowkey will be some combination of hostname, timestamp, and log-event - but what specifically? - -

+ Assume that the following data elements are being collected. + + + Hostname + + + Timestamp + + + Log event + + + Value/message + + + We can store them in an HBase table called LOG_DATA, but what will the rowkey be? From + these attributes the rowkey will be some combination of hostname, timestamp, and log-event - + but what specifically? +

Timestamp In The Rowkey Lead Position - The rowkey [timestamp][hostname][log-event] suffers from the monotonically increasing rowkey problem - described in . - - There is another pattern frequently mentioned in the dist-lists about “bucketing” timestamps, by performing a mod operation - on the timestamp. If time-oriented scans are important, this could be a useful approach. Attention must be paid to the number - of buckets, because this will require the same number of scans to return results. - + The rowkey [timestamp][hostname][log-event] suffers from the + monotonically increasing rowkey problem described in . + There is another pattern frequently mentioned in the dist-lists about “bucketing” + timestamps, by performing a mod operation on the timestamp. If time-oriented scans are + important, this could be a useful approach. Attention must be paid to the number of + buckets, because this will require the same number of scans to return results. + long bucket = timestamp % numBuckets; - - … to construct: - + + … to construct: + [bucket][timestamp][hostname][log-event] - - As stated above, to select data for a particular timerange, a Scan will need to be performed for each bucket. 100 buckets, - for example, will provide a wide distribution in the keyspace but it will require 100 Scans to obtain data for a single - timestamp, so there are trade-offs. - -

+ + As stated above, to select data for a particular timerange, a Scan will need to be + performed for each bucket. 100 buckets, for example, will provide a wide distribution in + the keyspace but it will require 100 Scans to obtain data for a single timestamp, so there + are trade-offs. +

+ +

Host In The Rowkey Lead Position - The rowkey [hostname][log-event][timestamp] is a candidate if there is a large-ish number of hosts to spread - the writes and reads across the keyspace. This approach would be useful if scanning by hostname was a priority. - -

+ The rowkey [hostname][log-event][timestamp] is a candidate if there is a + large-ish number of hosts to spread the writes and reads across the keyspace. This + approach would be useful if scanning by hostname was a priority. +

+ +

Timestamp, or Reverse Timestamp? - If the most important access path is to pull most recent events, then storing the timestamps as reverse-timestamps - (e.g., timestamp = Long.MAX_VALUE – timestamp) will create the property of being able to do a Scan on - [hostname][log-event] to obtain the quickly obtain the most recently captured events. - - Neither approach is wrong, it just depends on what is most appropriate for the situation. - - - Reverse Scan API - - HBASE-4811 implements an API to scan a table or a range within a table in reverse, reducing the need to optimize your schema for forward or reverse scanning. This feature is available in HBase 0.98 and later. See for more information. - - -

+ If the most important access path is to pull most recent events, then storing the + timestamps as reverse-timestamps (e.g.,

timestamp = Long.MAX_VALUE –
+            timestamp

) will create the property of being able to do a Scan on + [hostname][log-event] to obtain the quickly obtain the most recently + captured events. + Neither approach is wrong, it just depends on what is most appropriate for the + situation. + + Reverse Scan API + + HBASE-4811 + implements an API to scan a table or a range within a table in reverse, reducing the + need to optimize your schema for forward or reverse scanning. This feature is available + in HBase 0.98 and later. See + for more information. + +

+ +

Variangle Length or Fixed Length Rowkeys? - It is critical to remember that rowkeys are stamped on every column in HBase. If the hostname is “a” and the event type - is “e1” then the resulting rowkey would be quite small. However, what if the ingested hostname is - “myserver1.mycompany.com” and the event type is “com.package1.subpackage2.subsubpackage3.ImportantService”? - - It might make sense to use some substitution in the rowkey. There are at least two approaches: hashed and numeric. - In the Hostname In The Rowkey Lead Position example, it might look like this: - - Composite Rowkey With Hashes: - - [MD5 hash of hostname] = 16 bytes - [MD5 hash of event-type] = 16 bytes - [timestamp] = 8 bytes - - - Composite Rowkey With Numeric Substitution: - - For this approach another lookup table would be needed in addition to LOG_DATA, called LOG_TYPES. - The rowkey of LOG_TYPES would be: - - [type] (e.g., byte indicating hostname vs. event-type) - [bytes] variable length bytes for raw hostname or event-type. - - A column for this rowkey could be a long with an assigned number, which could be obtained by using an - HBase counter. - - So the resulting composite rowkey would be: - - [substituted long for hostname] = 8 bytes - [substituted long for event type] = 8 bytes - [timestamp] = 8 bytes - - In either the Hash or Numeric substitution approach, the raw values for hostname and event-type can be stored as columns. - -

+ It is critical to remember that rowkeys are stamped on every column in HBase. If the + hostname is “a” and the event type is “e1” then the resulting rowkey would be quite small. + However, what if the ingested hostname is “myserver1.mycompany.com” and the event type is + “com.package1.subpackage2.subsubpackage3.ImportantService”? + It might make sense to use some substitution in the rowkey. There are at least two + approaches: hashed and numeric. In the Hostname In The Rowkey Lead Position example, it + might look like this: + Composite Rowkey With Hashes: + + + [MD5 hash of hostname] = 16 bytes + + + [MD5 hash of event-type] = 16 bytes + + + [timestamp] = 8 bytes + + + Composite Rowkey With Numeric Substitution: + For this approach another lookup table would be needed in addition to LOG_DATA, called + LOG_TYPES. The rowkey of LOG_TYPES would be: + + + [type] (e.g., byte indicating hostname vs. event-type) + + + [bytes] variable length bytes for raw hostname or event-type. + + + A column for this rowkey could be a long with an assigned number, which could be + obtained by using an HBase + counter. + So the resulting composite rowkey would be: + + + [substituted long for hostname] = 8 bytes + + + [substituted long for event type] = 8 bytes + + + [timestamp] = 8 bytes + + + In either the Hash or Numeric substitution approach, the raw values for hostname and + event-type can be stored as columns. +

+ +

Case Study - Log Data and Timeseries Data on Steroids - This effectively is the OpenTSDB approach. What OpenTSDB does is re-write data and pack rows into columns for - certain time-periods. For a detailed explanation, see: http://opentsdb.net/schema.html, - and Lessons Learned from OpenTSDB - from HBaseCon2012. - - But this is how the general concept works: data is ingested, for example, in this manner… - + This effectively is the OpenTSDB approach. What OpenTSDB does is re-write data and pack + rows into columns for certain time-periods. For a detailed explanation, see: http://opentsdb.net/schema.html, and Lessons + Learned from OpenTSDB from HBaseCon2012. + But this is how the general concept works: data is ingested, for example, in this + manner… + [hostname][log-event][timestamp1] [hostname][log-event][timestamp2] [hostname][log-event][timestamp3] - - … with separate rowkeys for each detailed event, but is re-written like this… - - [hostname][log-event][timerange] - - … and each of the above events are converted into columns stored with a time-offset relative to the beginning timerange - (e.g., every 5 minutes). This is obviously a very advanced processing technique, but HBase makes this possible. - -

- -

+ + … with separate rowkeys for each detailed event, but is re-written like this… + [hostname][log-event][timerange] + … and each of the above events are converted into columns stored with a time-offset + relative to the beginning timerange (e.g., every 5 minutes). This is obviously a very + advanced processing technique, but HBase makes this possible. +

+ + +

Case Study - Customer/Order - Assume that HBase is used to store customer and order information. There are two core record-types being ingested: - a Customer record type, and Order record type. - - The Customer record type would include all the things that you’d typically expect: - - Customer number - Customer name - Address (e.g., city, state, zip) - Phone numbers, etc. - - - The Order record type would include things like: - - Customer number - Order number - Sales date - A series of nested objects for shipping locations and line-items (see - for details) - - - Assuming that the combination of customer number and sales order uniquely identify an order, these two attributes will compose - the rowkey, and specifically a composite key such as: - - [customer number][order number] - - … for a ORDER table. However, there are more design decisions to make: are the raw values the best choices for rowkeys? - - The same design questions in the Log Data use-case confront us here. What is the keyspace of the customer number, and what is the -format (e.g., numeric? alphanumeric?) As it is advantageous to use fixed-length keys in HBase, as well as keys that can support a -reasonable spread in the keyspace, similar options appear: - - Composite Rowkey With Hashes: + Assume that HBase is used to store customer and order information. There are two core + record-types being ingested: a Customer record type, and Order record type. + The Customer record type would include all the things that you’d typically expect: - [MD5 of customer number] = 16 bytes - [MD5 of order number] = 16 bytes + + Customer number + + + Customer name + + + Address (e.g., city, state, zip) + + + Phone numbers, etc. + - - Composite Numeric/Hash Combo Rowkey: + The Order record type would include things like: + + + Customer number + + + Order number + + + Sales date + + + A series of nested objects for shipping locations and line-items (see for details) + + + Assuming that the combination of customer number and sales order uniquely identify an + order, these two attributes will compose the rowkey, and specifically a composite key such + as: + [customer number][order number] + … for a ORDER table. However, there are more design decisions to make: are the + raw values the best choices for rowkeys? + The same design questions in the Log Data use-case confront us here. What is the + keyspace of the customer number, and what is the format (e.g., numeric? alphanumeric?) As it + is advantageous to use fixed-length keys in HBase, as well as keys that can support a + reasonable spread in the keyspace, similar options appear: + Composite Rowkey With Hashes: - [substituted long for customer number] = 8 bytes - [MD5 of order number] = 16 bytes + + [MD5 of customer number] = 16 bytes + + + [MD5 of order number] = 16 bytes + - -

- Single Table? Multiple Tables? - A traditional design approach would have separate tables for CUSTOMER and SALES. Another option is to pack multiple - record types into a single table (e.g., CUSTOMER++). - - Customer Record Type Rowkey: - - [customer-id] - [type] = type indicating ‘1’ for customer record type - - - Order Record Type Rowkey: - - [customer-id] - [type] = type indicating ‘2’ for order record type - [order] - - - The advantage of this particular CUSTOMER++ approach is that organizes many different record-types by customer-id - (e.g., a single scan could get you everything about that customer). The disadvantage is that it’s not as easy to scan for - a particular record-type. - + Composite Numeric/Hash Combo Rowkey: + + + [substituted long for customer number] = 8 bytes + + + [MD5 of order number] = 16 bytes + + +

+ Single Table? Multiple Tables? + A traditional design approach would have separate tables for CUSTOMER and SALES. + Another option is to pack multiple record types into a single table (e.g., CUSTOMER++). + Customer Record Type Rowkey: + + + [customer-id] + + + [type] = type indicating ‘1’ for customer record type + + + Order Record Type Rowkey: + + + [customer-id] + + + [type] = type indicating ‘2’ for order record type + + + [order] + + + The advantage of this particular CUSTOMER++ approach is that organizes many different + record-types by customer-id (e.g., a single scan could get you everything about that + customer). The disadvantage is that it’s not as easy to scan for a particular record-type. + +

+ Order Object Design + Now we need to address how to model the Order object. Assume that the class structure + is as follows: + + + Order + + (an Order can have multiple ShippingLocations + + + + LineItem + + (a ShippingLocation can have multiple LineItems + + + + ... there are multiple options on storing this data. +

+ Completely Normalized + With this approach, there would be separate tables for ORDER, SHIPPING_LOCATION, and + LINE_ITEM. + The ORDER table's rowkey was described above: + + The SHIPPING_LOCATION's composite rowkey would be something like this: + + + [order-rowkey] + + + [shipping location number] (e.g., 1st location, 2nd, etc.) + + + The LINE_ITEM table's composite rowkey would be something like this: + + + [order-rowkey] + + + [shipping location number] (e.g., 1st location, 2nd, etc.) + + + [line item number] (e.g., 1st lineitem, 2nd, etc.) + + + Such a normalized model is likely to be the approach with an RDBMS, but that's not + your only option with HBase. The cons of such an approach is that to retrieve + information about any Order, you will need: + + + Get on the ORDER table for the Order + + + Scan on the SHIPPING_LOCATION table for that order to get the ShippingLocation + instances + + + Scan on the LINE_ITEM for each ShippingLocation + + + ... granted, this is what an RDBMS would do under the covers anyway, but since there + are no joins in HBase you're just more aware of this fact. +

+ Single Table With Record Types + With this approach, there would exist a single table ORDER that would contain + The Order rowkey was described above: + + + [order-rowkey] + + + [ORDER record type] + + + The ShippingLocation composite rowkey would be something like this: + + + [order-rowkey] + + + [SHIPPING record type] + + + [shipping location number] (e.g., 1st location, 2nd, etc.) + + + The LineItem composite rowkey would be something like this: + + + [order-rowkey] + + + [LINE record type] + + + [shipping location number] (e.g., 1st location, 2nd, etc.) + + + [line item number] (e.g., 1st lineitem, 2nd, etc.) + + +

+ Denormalized + A variant of the Single Table With Record Types approach is to denormalize and + flatten some of the object hierarchy, such as collapsing the ShippingLocation attributes + onto each LineItem instance. + The LineItem composite rowkey would be something like this: + + + [order-rowkey] + + + [LINE record type] + + + [line item number] (e.g., 1st lineitem, 2nd, etc. - care must be taken that + there are unique across the entire order) + + + ... and the LineItem columns would be something like this: + + + itemNumber + + + quantity + + + price + + + shipToLine1 (denormalized from ShippingLocation) + + + shipToLine2 (denormalized from ShippingLocation) + + + shipToCity (denormalized from ShippingLocation) + + + shipToState (denormalized from ShippingLocation) + + + shipToZip (denormalized from ShippingLocation) + + + The pros of this approach include a less complex object heirarchy, but one of the + cons is that updating gets more complicated in case any of this information changes. +

- Order Object Design - Now we need to address how to model the Order object. Assume that the class structure is as follows: - -Order - ShippingLocation (an Order can have multiple ShippingLocations) - LineItem (a ShippingLocation can have multiple LineItems) - - ... there are multiple options on storing this data. - -

- Completely Normalized - With this approach, there would be separate tables for ORDER, SHIPPING_LOCATION, and LINE_ITEM. - - The ORDER table's rowkey was described above: - - The SHIPPING_LOCATION's composite rowkey would be something like this: - - [order-rowkey] - [shipping location number] (e.g., 1st location, 2nd, etc.) - - - The LINE_ITEM table's composite rowkey would be something like this: - - [order-rowkey] - [shipping location number] (e.g., 1st location, 2nd, etc.) - [line item number] (e.g., 1st lineitem, 2nd, etc.) - - - Such a normalized model is likely to be the approach with an RDBMS, but that's not your only option with HBase. - The cons of such an approach is that to retrieve information about any Order, you will need: - - Get on the ORDER table for the Order - Scan on the SHIPPING_LOCATION table for that order to get the ShippingLocation instances - Scan on the LINE_ITEM for each ShippingLocation - - ... granted, this is what an RDBMS would do under the covers anyway, but since there are no joins in HBase - you're just more aware of this fact. - -

- Single Table With Record Types - With this approach, there would exist a single table ORDER that would contain - - The Order rowkey was described above: - - [order-rowkey] - [ORDER record type] - - - The ShippingLocation composite rowkey would be something like this: - - [order-rowkey] - [SHIPPING record type] - [shipping location number] (e.g.,