From 11ddafabfea0e2278487f614255298912f5f5547 Mon Sep 17 00:00:00 2001 From: Biswajit Ghosh <34703680+Biswajitghosh98@users.noreply.github.com> Date: Thu, 6 May 2021 12:27:48 +0530 Subject: [PATCH 1/8] Update 202011_vertical_block_sharding.md --- .../202011_vertical_block_sharding.md | 32 +++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/docs/proposals/202011_vertical_block_sharding.md b/docs/proposals/202011_vertical_block_sharding.md index 20d4fde6c9..088a596ac0 100644 --- a/docs/proposals/202011_vertical_block_sharding.md +++ b/docs/proposals/202011_vertical_block_sharding.md @@ -77,6 +77,7 @@ index size). * Automatically cap the size of index per block to X GB without impacting read performance. * Optional: CLI for vertical sharding of old blocks. +* Alternatively, increase the number of compactions before index size hits the current limitations, to improve performance of querying. ## Non Goals @@ -87,13 +88,40 @@ index size). ## Proposal -**We propose to vertically shard all blocks which goes beyond the specified index size limit during the compaction process.** +**We propose to vertically shard all blocks which goes beyond the specified index size limit during the compaction process, else efficiently shard the blocks at compaction level 0.** * Special metadata in meta.json indicating a shard. -TBD +#### Approach 1: Sharding at compaction level 0 +The idea behind this approach is to use a hash function which takes the metric_name as an input, and returns an integer value in the range of 1 to X (X denoting the number of shards). As of now, X is static and has been set to an experimental value of 10. We would also like decide whether keeping X dynamic would be a better approach. To do that we would have to include some sort of consistent hashing mechanism so that X can be changed dynamically as per the need of the user. +For this approach, we'd begin by adding an external label, `hash_number` during block creation, which would denote the shard number. We can keep it 0, because subsequently the hash function would return values only between 1 and X (inclusive), so there won't be any chance of a possible clash. During the first compaction of the initially created 2h blocks, we'll divide each block into X blocks. The block number i (1<=i<=X) would contain only those series whose hash value of mertic names is i. + +Once this step is completed, we can be certain that all the series that belong to a certain block have the same `hash_number`. Now, we'll allow grouper and planner to group only those blocks together which have the same `hash_number`. This would serve us two purposes : + + * Allow grouper to group blocks in such a way that we can run X concurrent compactor processes. + * Intra-shard horizontal and vertical compaction would mean less variance in metrics and hence we can expect a larger block in terms of the difference in `maxt` and `mint` when a certain blocks index hits the limit, and then marked with `noCompact`. + +#### Approach 2: Sharding a block when its index size hits the limit + +This approach is a slight modification to Apporach 1. +Instead of creating just one external label `hash_number`, we can create a series of hash_numbers and corresponding `hash_function`s. +This is also a static method, but an optimization over Approach 1. +The way it would work is, intially during block creation, we'll add `n` external labels +``` +hash_number_1 : 0 +hash_number_2 : 0 +. +. +. +hash_number_n : 0 +``` +Wherein, n is the number of times we want a blocks index to hit the limit. +The current method ignores a block for further compaction once its index reaches a limit. We'd let that stay as it is, with a caveat that whenever a blocks index size hits the limit, we'd find the first 0-valued hash_number (let's say i), then further sub-divide (shard) the existing block with the corresponding `hash_function_i`, and then let the compaction process run as usual. +Here also, we'd allow grouper and planner to compact (horizontally and vertically) only those blocks together whose `hash_number` external labels match completely. +The advantage of this method is, that it would allow us to help grow the size of the block in terms of time difference (`maxt - mint`) exponentially, so we can make an estimated guess that n would not exceed 3 or 4. +The negative aspect of this approach would be, as blocks grow larger, it would become difficult to find a subsequent block to compact it with. ## Alternatives From 5de0618c0d6c83ae565b73ed0de373546ee6ff7e Mon Sep 17 00:00:00 2001 From: Biswajit Ghosh <34703680+Biswajitghosh98@users.noreply.github.com> Date: Thu, 6 May 2021 13:21:02 +0530 Subject: [PATCH 2/8] Update 202011_vertical_block_sharding.md --- .../202011_vertical_block_sharding.md | 24 ++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/docs/proposals/202011_vertical_block_sharding.md b/docs/proposals/202011_vertical_block_sharding.md index 088a596ac0..833912edbb 100644 --- a/docs/proposals/202011_vertical_block_sharding.md +++ b/docs/proposals/202011_vertical_block_sharding.md @@ -123,9 +123,31 @@ Here also, we'd allow grouper and planner to compact (horizontally and verticall The advantage of this method is, that it would allow us to help grow the size of the block in terms of time difference (`maxt - mint`) exponentially, so we can make an estimated guess that n would not exceed 3 or 4. The negative aspect of this approach would be, as blocks grow larger, it would become difficult to find a subsequent block to compact it with. +### Design Decisions (Pros and Cons) + +#### Decision 1: Shard always by X (static), for everything +* Pros + * We can grow the time window a block spans, even if total index size reaches cap limit, because we vertically shard it into X blocks. + * The compaction process is simple and predictable, as compaction of any sort would take place between blocks that belong to the same shard. +* Cons + * The problem of index size hitting limit is still present. + * Statically sharding the block into X can be an overkill (if X is too large, say 10 for a few cases), or it might underperform (if X is as less as 2 or 3) for some massive TSDB with larger magnitude of metric numbers. + +#### Decision 2: Using metric name as the only parameter for hash_function +* Pros + * It is simple to implement and can prove to be quite efficient while querying because metric names can be obtained from the query itself, and calculating the corresponding hash would take minimal time as well. + * For deeper hash layers, we can statistically be certain of having an even distribution (for Approach 2). +* Cons + * If the nested hashing is shallow, then there's a possibility of uneven sharding. + ## Alternatives -* Shard number stored in external label. +* Keep the system as it is. +* In the above approach, keep the number of shards dynamic, depending on the use case. + * Pros + * We can dynamically adjust to the situation. If a certain block stream has only few metrics, we can keep 1 shard, and if during the 2w period, suddenly we have 100 millions series (or some number beyond index size), we can horizontally split to 10 or more. And then later back to 1. + * Cons + * With dynamically changing metas, it would become difficult to group blocks together for vertical compaction. ## Future Work From c41d3ffe2eb0791d0eb30b36dec50803ab0b2daf Mon Sep 17 00:00:00 2001 From: Biswajit Ghosh <34703680+Biswajitghosh98@users.noreply.github.com> Date: Thu, 6 May 2021 14:35:04 +0530 Subject: [PATCH 3/8] Update 202011_vertical_block_sharding.md --- docs/proposals/202011_vertical_block_sharding.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/proposals/202011_vertical_block_sharding.md b/docs/proposals/202011_vertical_block_sharding.md index 833912edbb..afb77b7f6b 100644 --- a/docs/proposals/202011_vertical_block_sharding.md +++ b/docs/proposals/202011_vertical_block_sharding.md @@ -90,7 +90,7 @@ index size). **We propose to vertically shard all blocks which goes beyond the specified index size limit during the compaction process, else efficiently shard the blocks at compaction level 0.** -* Special metadata in meta.json indicating a shard. +* We'll be using a special metadata in meta.json indicating a shard. #### Approach 1: Sharding at compaction level 0 From 99429e9be8e2f65172fca5674abc4d1b9e5af0a5 Mon Sep 17 00:00:00 2001 From: Biswajit Ghosh <34703680+Biswajitghosh98@users.noreply.github.com> Date: Tue, 11 May 2021 04:24:15 +0530 Subject: [PATCH 4/8] Update 202011_vertical_block_sharding.md --- .../202011_vertical_block_sharding.md | 88 ++++++------------- 1 file changed, 27 insertions(+), 61 deletions(-) diff --git a/docs/proposals/202011_vertical_block_sharding.md b/docs/proposals/202011_vertical_block_sharding.md index afb77b7f6b..88deec13ff 100644 --- a/docs/proposals/202011_vertical_block_sharding.md +++ b/docs/proposals/202011_vertical_block_sharding.md @@ -3,7 +3,7 @@ title: "Vertical Block Sharding" type: proposal menu: proposals status: proposed -owner: @bwplotka +owner: @bwplotka @biswajitghosh98 Date: 2 November 2020 --- @@ -75,9 +75,8 @@ index size). ## Goals -* Automatically cap the size of index per block to X GB without impacting read performance. - * Optional: CLI for vertical sharding of old blocks. -* Alternatively, increase the number of compactions before index size hits the current limitations, to improve performance of querying. +* Automatically cap the size of index per block to X GB without impacting read performance. +* Alternatively, allow grouping series within blocks that spans over larger time windows defined in compaction level (e.g up to 2w), to leverage downsampling for querying. ## Non Goals @@ -88,66 +87,33 @@ index size). ## Proposal -**We propose to vertically shard all blocks which goes beyond the specified index size limit during the compaction process, else efficiently shard the blocks at compaction level 0.** +**We propose to vertically shard blocks when it reaches the cap limit in terms of number of series or size of index file, and then adaptively decide the number of shards depending on the users requirement** * We'll be using a special metadata in meta.json indicating a shard. -#### Approach 1: Sharding at compaction level 0 - -The idea behind this approach is to use a hash function which takes the metric_name as an input, and returns an integer value in the range of 1 to X (X denoting the number of shards). As of now, X is static and has been set to an experimental value of 10. We would also like decide whether keeping X dynamic would be a better approach. To do that we would have to include some sort of consistent hashing mechanism so that X can be changed dynamically as per the need of the user. - -For this approach, we'd begin by adding an external label, `hash_number` during block creation, which would denote the shard number. We can keep it 0, because subsequently the hash function would return values only between 1 and X (inclusive), so there won't be any chance of a possible clash. During the first compaction of the initially created 2h blocks, we'll divide each block into X blocks. The block number i (1<=i<=X) would contain only those series whose hash value of mertic names is i. - -Once this step is completed, we can be certain that all the series that belong to a certain block have the same `hash_number`. Now, we'll allow grouper and planner to group only those blocks together which have the same `hash_number`. This would serve us two purposes : - - * Allow grouper to group blocks in such a way that we can run X concurrent compactor processes. - * Intra-shard horizontal and vertical compaction would mean less variance in metrics and hence we can expect a larger block in terms of the difference in `maxt` and `mint` when a certain blocks index hits the limit, and then marked with `noCompact`. - -#### Approach 2: Sharding a block when its index size hits the limit - -This approach is a slight modification to Apporach 1. -Instead of creating just one external label `hash_number`, we can create a series of hash_numbers and corresponding `hash_function`s. -This is also a static method, but an optimization over Approach 1. -The way it would work is, intially during block creation, we'll add `n` external labels -``` -hash_number_1 : 0 -hash_number_2 : 0 -. -. -. -hash_number_n : 0 -``` -Wherein, n is the number of times we want a blocks index to hit the limit. -The current method ignores a block for further compaction once its index reaches a limit. We'd let that stay as it is, with a caveat that whenever a blocks index size hits the limit, we'd find the first 0-valued hash_number (let's say i), then further sub-divide (shard) the existing block with the corresponding `hash_function_i`, and then let the compaction process run as usual. -Here also, we'd allow grouper and planner to compact (horizontally and vertically) only those blocks together whose `hash_number` external labels match completely. -The advantage of this method is, that it would allow us to help grow the size of the block in terms of time difference (`maxt - mint`) exponentially, so we can make an estimated guess that n would not exceed 3 or 4. -The negative aspect of this approach would be, as blocks grow larger, it would become difficult to find a subsequent block to compact it with. - -### Design Decisions (Pros and Cons) - -#### Decision 1: Shard always by X (static), for everything -* Pros - * We can grow the time window a block spans, even if total index size reaches cap limit, because we vertically shard it into X blocks. - * The compaction process is simple and predictable, as compaction of any sort would take place between blocks that belong to the same shard. -* Cons - * The problem of index size hitting limit is still present. - * Statically sharding the block into X can be an overkill (if X is too large, say 10 for a few cases), or it might underperform (if X is as less as 2 or 3) for some massive TSDB with larger magnitude of metric numbers. - -#### Decision 2: Using metric name as the only parameter for hash_function -* Pros - * It is simple to implement and can prove to be quite efficient while querying because metric names can be obtained from the query itself, and calculating the corresponding hash would take minimal time as well. - * For deeper hash layers, we can statistically be certain of having an even distribution (for Approach 2). -* Cons - * If the nested hashing is shallow, then there's a possibility of uneven sharding. - -## Alternatives - -* Keep the system as it is. -* In the above approach, keep the number of shards dynamic, depending on the use case. - * Pros - * We can dynamically adjust to the situation. If a certain block stream has only few metrics, we can keep 1 shard, and if during the 2w period, suddenly we have 100 millions series (or some number beyond index size), we can horizontally split to 10 or more. And then later back to 1. - * Cons - * With dynamically changing metas, it would become difficult to group blocks together for vertical compaction. +### Adaptive Binary Sharding + +The idea behind this approach is to leverage on the structural benefits of a binary tree to adaptively shard blocks which hit the maximum limit, either in terms of index size or number of series (set by user). + +For this approach, we'd need to add two extra external label `hash_number`, initialized to 0, `shard_level` initialized to 0, denoting the number of times it has been sharded during the compaction process, and a set of 16 `hash_function`s. The reason for chosing 16 is because 2 raised to the power 16 is 65536, which is a logical upper bound for the number of shards a user might want to have. + +We'll be using `hash_level` and `hash_number` to group and plan blocks to compact together. The way it'd work is, we'll allow grouper to group only those blocks together whose `hash_level`s are same. Also, we'd allow planner to compact only those blocks together that share the same `hash_number`. So, with this, the compactor would run as it is unless a compacted block has hit the limit for the first time. + + + +We're allowing a compacted block to hit the limit for a maximum of 16 times (if not specified otherwise by the user), and at the 16th level, if/when it further compacts and hits the limit, we're marking it for no more further compactions. + +We can keep growing the binary tree by recursively using the same logic. The reason for calling this adaptive is because at any point of time, the total number of shards is equal to the number of nodes on the tree (say x). If any of the leaves overloads (a block in that particular shards hits the limit), that particular leaf would split into 2, effectively increasing the number of shards to x+2, hence providing just the right amount of shards to get the job done. + + + +### Pros and cons + +### Design Decisions + +#### Decision 1: Using size of index file vs number of metrics in a block as an indicator for sharding + +The pros and cons for selecting one design choice over the other is yet to be discovered, and would be more clear after implementation and testing. ## Future Work From d4d511db2a7533af1e07d34d5730087f5832d312 Mon Sep 17 00:00:00 2001 From: Biswajit Ghosh <34703680+Biswajitghosh98@users.noreply.github.com> Date: Tue, 11 May 2021 08:12:25 +0530 Subject: [PATCH 5/8] Update 202011_vertical_block_sharding.md --- .../proposals/202011_vertical_block_sharding.md | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/docs/proposals/202011_vertical_block_sharding.md b/docs/proposals/202011_vertical_block_sharding.md index 88deec13ff..88ad87a112 100644 --- a/docs/proposals/202011_vertical_block_sharding.md +++ b/docs/proposals/202011_vertical_block_sharding.md @@ -101,14 +101,29 @@ We'll be using `hash_level` and `hash_number` to group and plan blocks to compac -We're allowing a compacted block to hit the limit for a maximum of 16 times (if not specified otherwise by the user), and at the 16th level, if/when it further compacts and hits the limit, we're marking it for no more further compactions. +We're allowing a compacted block to hit the limit for a maximum of 16 times (if not specified otherwise by the user), and at the 16th level, if/when it further compacts and hits the limit, we're marking it for no more further compactions. The algorithm for sharding would come into play once a compacted block is declared invalid, or its size hits the limit. If the compacted block is still valid, but its index size is too large (or the number of series it holds exceeds the limit decided by the user), we'll look at the current `hash_level`(say `hl`) of the compacted block (if valid), or the blocks participating in the compaction process (if the compacted block is invalid), then we'll either shard the compacted block or the participating blocks (depending on the situation), `using hash_function[hl+1]`, and hence set the `hash_level` of the newly created blocks to `hl+1`, and then upload them for further compaction processes, if `hl` is not equal to `max_shard_level_limit`, which is 16 by default. In case `hl` is equal to `max_shard_level_limit`, we will take one of the 2 decisions depending on whether the compacted block is valid or invalid. +* If the compacted block is valid, then continue the usual compaction process until it results into an invalid block. +* If the compacted block is invalid, then add the no compact filter to all the participating blocks. We can keep growing the binary tree by recursively using the same logic. The reason for calling this adaptive is because at any point of time, the total number of shards is equal to the number of nodes on the tree (say x). If any of the leaves overloads (a block in that particular shards hits the limit), that particular leaf would split into 2, effectively increasing the number of shards to x+2, hence providing just the right amount of shards to get the job done. +To give the user control over selecting number of shards (if needed), we can add a flag `max_shard_level_limit`, and the user can change the value from the default value of 16 to anything between 0 and 16 (inclusive). Let the current value for `max_shard_level_limit` be `max_l`. Then, instead of compactors grouper grouping blocks with the same `shard_level` together, it'd group blocks on the basis of same `max(shard_level,max_l)`, hence reverting the sharding process in a controlled manner. A similar logic would work if we decide to increase the value of `max_shard_level_limit`. + ### Pros and cons +#### Pros + +* This layered sharding approach would statistically give more evenly distributed shards as compared to using only one hash function and hence sharding the blocks into some x shards. For layered binary sharding, let's assume that all the 16 hash functions are bad hash functions, and each one of them gives a skewed distribution, even then we'll get a fairly even distribution after sharding through all the layers. This is pictorially explained below +* The process is adaptive, hence any user can opt to go for default configuration and not worry about setting the number of shards. +* If for some reasons, the user decides to decrease the number of shards, it can be done by setting a `max_shard_level_limit` to any value between 0 and 16. + +#### Cons + +* We didn't solve the problem completely, but we delayed it as much as we could to get some benefits out of the delaying process. +* Cases were large number of blocks participate in a compaction process and result in an invalid block would cause the sharding algorithm to be triggered, even though sharding at that point might not be necessary. + ### Design Decisions #### Decision 1: Using size of index file vs number of metrics in a block as an indicator for sharding From a18ad221c031f142e8d5e58aabe7c4ace4fe8aa8 Mon Sep 17 00:00:00 2001 From: Biswajit Ghosh <34703680+Biswajitghosh98@users.noreply.github.com> Date: Tue, 11 May 2021 08:13:31 +0530 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zQt)V@!k3_Au zm491b{;wid*k!KmkD~a`xBvg>f9Et>Wjn~Xt$J?#e%~&9;Q)MLbPTmiHEo{$2WYF7 ArT_o{ literal 0 HcmV?d00001 From c88e9a45e4d5a15d4615b05b05e2a6f158f0e12f Mon Sep 17 00:00:00 2001 From: Biswajit Ghosh <34703680+Biswajitghosh98@users.noreply.github.com> Date: Tue, 11 May 2021 08:20:30 +0530 Subject: [PATCH 7/8] Update 202011_vertical_block_sharding.md --- docs/proposals/202011_vertical_block_sharding.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/proposals/202011_vertical_block_sharding.md b/docs/proposals/202011_vertical_block_sharding.md index 88ad87a112..e1614abdb7 100644 --- a/docs/proposals/202011_vertical_block_sharding.md +++ b/docs/proposals/202011_vertical_block_sharding.md @@ -99,7 +99,7 @@ For this approach, we'd need to add two extra external label `hash_number`, init We'll be using `hash_level` and `hash_number` to group and plan blocks to compact together. The way it'd work is, we'll allow grouper to group only those blocks together whose `hash_level`s are same. Also, we'd allow planner to compact only those blocks together that share the same `hash_number`. So, with this, the compactor would run as it is unless a compacted block has hit the limit for the first time. - +![vbs-image1](../img/vertical_block_sharding_image1.png) We're allowing a compacted block to hit the limit for a maximum of 16 times (if not specified otherwise by the user), and at the 16th level, if/when it further compacts and hits the limit, we're marking it for no more further compactions. The algorithm for sharding would come into play once a compacted block is declared invalid, or its size hits the limit. If the compacted block is still valid, but its index size is too large (or the number of series it holds exceeds the limit decided by the user), we'll look at the current `hash_level`(say `hl`) of the compacted block (if valid), or the blocks participating in the compaction process (if the compacted block is invalid), then we'll either shard the compacted block or the participating blocks (depending on the situation), `using hash_function[hl+1]`, and hence set the `hash_level` of the newly created blocks to `hl+1`, and then upload them for further compaction processes, if `hl` is not equal to `max_shard_level_limit`, which is 16 by default. In case `hl` is equal to `max_shard_level_limit`, we will take one of the 2 decisions depending on whether the compacted block is valid or invalid. * If the compacted block is valid, then continue the usual compaction process until it results into an invalid block. @@ -107,7 +107,8 @@ We're allowing a compacted block to hit the limit for a maximum of 16 times (if We can keep growing the binary tree by recursively using the same logic. The reason for calling this adaptive is because at any point of time, the total number of shards is equal to the number of nodes on the tree (say x). If any of the leaves overloads (a block in that particular shards hits the limit), that particular leaf would split into 2, effectively increasing the number of shards to x+2, hence providing just the right amount of shards to get the job done. - +![vbs-image2](../img/vertical_block_sharding_image2.png) +![vbs-image3](../img/vertical_block_sharding_image3.png) To give the user control over selecting number of shards (if needed), we can add a flag `max_shard_level_limit`, and the user can change the value from the default value of 16 to anything between 0 and 16 (inclusive). Let the current value for `max_shard_level_limit` be `max_l`. Then, instead of compactors grouper grouping blocks with the same `shard_level` together, it'd group blocks on the basis of same `max(shard_level,max_l)`, hence reverting the sharding process in a controlled manner. A similar logic would work if we decide to increase the value of `max_shard_level_limit`. @@ -115,7 +116,7 @@ To give the user control over selecting number of shards (if needed), we can add #### Pros -* This layered sharding approach would statistically give more evenly distributed shards as compared to using only one hash function and hence sharding the blocks into some x shards. For layered binary sharding, let's assume that all the 16 hash functions are bad hash functions, and each one of them gives a skewed distribution, even then we'll get a fairly even distribution after sharding through all the layers. This is pictorially explained below +* This layered sharding approach would statistically give more evenly distributed shards as compared to using only one hash function and hence sharding the blocks into some x shards. For layered binary sharding, let's assume that all the 16 hash functions are bad hash functions, and each one of them gives a skewed distribution, even then we'll get a fairly even distribution after sharding through all the layers. This is pictorially explained below ![vbs-image4](../img/vertical_block_sharding_image4.png) * The process is adaptive, hence any user can opt to go for default configuration and not worry about setting the number of shards. * If for some reasons, the user decides to decrease the number of shards, it can be done by setting a `max_shard_level_limit` to any value between 0 and 16. From 68e3413265dbf35ef4716b546d61e66debbb8ff1 Mon Sep 17 00:00:00 2001 From: Biswajit Ghosh <34703680+Biswajitghosh98@users.noreply.github.com> Date: Mon, 17 May 2021 00:51:36 +0530 Subject: [PATCH 8/8] Update docs/proposals/202011_vertical_block_sharding.md Co-authored-by: Hitanshu Mehta <44025541+hitanshu-mehta@users.noreply.github.com> --- docs/proposals/202011_vertical_block_sharding.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/proposals/202011_vertical_block_sharding.md b/docs/proposals/202011_vertical_block_sharding.md index e1614abdb7..25deb16067 100644 --- a/docs/proposals/202011_vertical_block_sharding.md +++ b/docs/proposals/202011_vertical_block_sharding.md @@ -123,7 +123,7 @@ To give the user control over selecting number of shards (if needed), we can add #### Cons * We didn't solve the problem completely, but we delayed it as much as we could to get some benefits out of the delaying process. -* Cases were large number of blocks participate in a compaction process and result in an invalid block would cause the sharding algorithm to be triggered, even though sharding at that point might not be necessary. +* Cases where large number of blocks participate in a compaction process and result in an invalid block would cause the sharding algorithm to be triggered, even though sharding at that point might not be necessary. ### Design Decisions